Negative Binomial Random Effects Research Articles

Gender Equality and State Executions Within and Across 100 Countries From 1997 to 2010

The achievement of gender equality requires strong support from the state and changes, in turn, the behavior of the state in both domestic and international arenas. Research suggests that an increased representation of women in government provides more ethical governance as well as greater state pacifism in international conflicts. While research has shown that women are more likely than men to oppose death penalty, no study has examined the influence of women’s representation in government on executions carried out by the state. Using a modified negative binomial random-effects model, this study examines gender equality as a predictor of executions and fills the growing need for longitudinal analysis of executions by using data from 100 countries over 14 years. The results indicate that increases in women’s representation in parliament are negatively associated with executions over time and are conditioned by the level of democracy between countries.

Potential Economic Benefits of Paid Sick Leave in Reducing Absenteeism Related to the Spread of Influenza-Like Illness.

Most U.S. employers are not required to provide paid sick leave (PSL), and there is limited information on the economic return of providing PSL. We estimated potential benefits to employers of PSL in reducing absenteeism related to the spread of influenza-like illness (ILI). We used nationally representative data and a negative binomial random effects model to estimate the impact of PSL in reducing overall absence due to illness or injury. We used published data to compute the share of ILI from the total days of absence, ILI transmission rates at workplaces, wages, and other parameters. Providing PSL could have saved employers $0.63 to $1.88 billion in reduced ILI-related absenteeism costs per year during 2007 to 2014 in 2016 dollars. These findings might help employers consider PSL as an investment rather than as a cost without any return.

Predicting expressway crash frequency using a random effect negative binomial model: A case study in China

To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50km long expressway in China, including 567 crash records (2006–2008), were collected and analyzed. Both the fixed-length and the homogeneous longitudinal grade methods were applied to divide the study expressway section into segments. A negative binomial (NB) model and a random effect negative binomial (RENB) model were developed to predict crash frequency. The parameters of both models were determined using the maximum likelihood (ML) method, and the mixed stepwise procedure was applied to examine the significance of explanatory variables. Three explanatory variables, including longitudinal grade, road width, and ratio of longitudinal grade and curve radius (RGR), were found as significantly affecting crash frequency. The marginal effects of significant explanatory variables to the crash frequency were analyzed. The model performance was determined by the relative prediction error and the cumulative standardized residual. The results show that the RENB model outperforms the NB model. It was also found that the model performance with the fixed-length segment method is superior to that with the homogeneous longitudinal grade segment method.

Spatiotemporal and random parameter panel data models of traffic crash fatalities in Vietnam

This paper investigates factors associated with traffic crash fatalities in 63 provinces of Vietnam during the period from 2012 to 2014. Random effect negative binomial (RENB) and random parameter negative binomial (RPNB) panel data models are adopted to consider spatial heterogeneity across provinces. In addition, a spatiotemporal model with conditional autoregressive priors (ST-CAR) is utilised to account for spatiotemporal autocorrelation in the data. The statistical comparison indicates the ST-CAR model outperforms the RENB and RPNB models. Estimation results provide several significant findings. For example, traffic crash fatalities tend to be higher in provinces with greater numbers of level crossings. Passenger distance travelled and road lengths are also positively associated with fatalities. However, hospital densities are negatively associated with fatalities. The safety impact of the national highway 1A, the main transport corridor of the country, is also highlighted.

Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia

Safety is a key concern in the design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyze crash frequency data obtained from Yarra Trams, the tram operator in Melbourne. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that group specific effects are randomly distributed across locations. The results identify many significant factors effecting tram-involved crash frequency including tram service frequency (2.71), tram stop spacing (−0.42), tram route section length (0.31), tram signal priority (−0.25), general traffic volume (0.18), tram lane priority (−0.15) and ratio of platform tram stops (−0.09). Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.

Investigating Pedestrian Injury Crashes on Modern Roundabouts in Addis Ababa, Ethiopia

Pedestrian crashes represent about 40% of total fatal crashes in low-income developing countries. Although many pedestrian crashes in these countries occur at unsignalized intersections such as roundabouts, studies focusing on this issue are limited. The objective of this study was to develop safety performance functions for pedestrian crashes at modern roundabouts to identify significant roadway geometric, traffic, and land use characteristics related to pedestrian safety. Detailed data, including various forms of exposure, geometric and traffic characteristics, and spatial factors such as proximity to schools and to drinking establishments were collected from a sample of 22 modern roundabouts in Addis Ababa, Ethiopia, representing about 56% of such roundabouts in Addis Ababa. To account for spatial correlation resulting from multiple observations at a roundabout, both the random effect Poisson (REP) and random effect negative binomial (RENB) regression models were estimated. Model goodness-of-fit statistics revealed a marginally superior fit of the REP model to the data compared with the RENB model. Pedestrian crossing volume and the product of traffic volumes along major and minor roads had significant and positive associations with pedestrian crashes at roundabouts. The presence of a public transport (bus or taxi) terminal beside a roundabout was associated with increased pedestrian crashes. Although the maximum gradient of an approach road was negatively associated with pedestrian safety, the provision of a raised median along an approach appeared to increase pedestrian safety at roundabouts. Remedial measures were identified for combating pedestrian safety problems at roundabouts in the context of a developing country.

Balancing the Two Knowledge Dimensions in Innovation Efforts: An Empirical Examination among Pharmaceutical Firms

This study investigates the relationships between the two knowledge dimensions (knowledge breadth and knowledge depth) and two types of innovations (radical innovation and incremental innovation). While existing literature identifies knowledge in general as an important driver of innovation, the exact relationships between knowledge breadth/depth and incremental/radical innovations are not clear. Drawing from the knowledge‐based view, this study advances the understanding of the relationships between knowledge dimensions and types of innovations by hypothesizing a nonlinear relationship between knowledge breadth and radical innovation as well as a nonlinear relationship between knowledge depth and incremental innovation. Furthermore, the moderating effects of the interaction between knowledge breadth and knowledge depth on the above‐mentioned relationships are also examined. Due to the different natures of the two types of innovations, it is hypothesized that knowledge depth positively moderates the relationship between knowledge breadth and radical innovation while knowledge breadth negatively moderates the relationship between knowledge depth and incremental innovation. To empirically test the hypotheses, secondary data from multiple sources were collected on 64 pharmaceutical firms over 15 years. Due to the panel data structure and observed dispersion issues in the dependent variables, negative binomial random effects models were formulated to test the hypotheses. The statistical results largely support the proposed hypotheses. The results demonstrate that while knowledge breadth positively contributes to the development of radical innovations and knowledge depth positively contributes to the development of incremental innovations, both relationships are subject to diminishing returns. Furthermore, while the finding did support the negative moderating effect of the knowledge breadth on incremental innovation, the positive moderating effect of knowledge depth on radical innovation is not supported. While the effect is not explicitly hypothesized, knowledge breadth seems to have a direct impact on incremental innovation as well.

Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian Federal Roads

Head-on crashes are among the most severe collision types and of great concern to road safety authorities. Therefore, it justifies more efforts to reduce both the frequency and severity of this collision type. To this end, it is necessary to first identify factors associating with the crash occurrence. This can be done by developing crash prediction models that relate crash outcomes to a set of contributing factors. This study intends to identify the factors affecting both the frequency and severity of head-on crashes that occurred on 448 segments of five federal roads in Malaysia. Data on road characteristics and crash history were collected on the study segments during a 4-year period between 2007 and 2010. The frequency of head-on crashes were fitted by developing and comparing seven count-data models including Poisson, standard negative binomial (NB), random-effect negative binomial, hurdle Poisson, hurdle negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. To model crash severity, a random-effect generalized ordered probit model (REGOPM) was used given a head-on crash had occurred. With respect to the crash frequency, the random-effect negative binomial (RENB) model was found to outperform the other models according to goodness of fit measures. Based on the results of the model, the variables horizontal curvature, terrain type, heavy-vehicle traffic, and access points were found to be positively related to the frequency of head-on crashes, while posted speed limit and shoulder width decreased the crash frequency. With regard to the crash severity, the results of REGOPM showed that horizontal curvature, paved shoulder width, terrain type, and side friction were associated with more severe crashes, whereas land use, access points, and presence of median reduced the probability of severe crashes. Based on the results of this study, some potential countermeasures were proposed to minimize the risk of head-on crashes.

A Quantitative Study of the Effect of Foreign Investment on Nonresident Patenting in Developing Countries, 1980-2010

This study examines whether foreign investment is a significant predictor of nonresident patent counts in developing countries over time. Unbalanced panel data are observed for 64 developing countries over a 31 year time period from 1980 to 2010 to test a hypothesis stating that countries with higher levels of inward accumulated foreign direct investment (FDI) stocks have higher nonresident patent counts, controlling for other explanatory factors such as population size, level of economic development, education rates, government expenditures, levels of domestic investment, and resident patent counts. Predicted counts of nonresident patents are estimated with Random Effects Negative Binomial (RENB) regression to model the effects of different independent variables on nonresident patenting in each country. Results confirm this study’s hypothesis by showing a significant and robust positive effect for accumulated FDI stocks on nonresident patent counts, while controlling for the effects of other variables over time in the sample of 64 developing countries included in this analysis. This study advances a sociological approach to theories of intellectual property and posits the accumulation of FDI stock as critical to predicting patterns of ownership of intangible capital wealth in developing countries over time. It remains to be seen if developed country FDI influences nonresident patent activities in similar or different ways to developing countries. Future studies may compare between groups of developed and developing countries to determine if nonresident and resident patent activities evolve in the same fashion. Finally, future research may also focus on the nuances between different types of intellectual property (patents, copyrights, trademarks, etc.) in order to more accurately predict each class of intangible capital wealth.

Profitability and occupational injuries in U.S. underground coal mines

BackgroundCoal plays a crucial role in the U.S. economy yet underground coal mining continues to be one of the most dangerous occupations in the country. In addition, there are large variations in both profitability and the incidence of occupational injuries across mines. ObjectiveThe objective of this study was to examine the association between profitability and the incidence rate of occupational injuries in U.S. underground coal mines between 1992 and 2008. Data and methodWe used mine-specific data on annual hours worked, geographic location, and the number of occupational injuries suffered annually from the employment and accident/injury databases of the Mine Safety and Health Administration, and mine-specific data on annual revenue from coal sales, mine age, workforce union status, and mining method from the U.S. Energy Information Administration. A total of 5669 mine-year observations (number of mines×number of years) were included in our analysis. We used a negative binomial random effects model that was appropriate for analyzing panel (combined time-series and cross-sectional) injury data that were non-negative and discrete. The dependent variable, occupational injury, was measured in three different and non-mutually exclusive ways: all reported fatal and nonfatal injuries, reported nonfatal injuries with lost workdays, and the ‘most serious’ (i.e. sum of fatal and serious nonfatal) injuries reported. The total number of hours worked in each mine and year examined was used as an exposure variable. Profitability, the main explanatory variable, was approximated by revenue per hour worked. Our model included mine age, workforce union status, mining method, and geographic location as additional control variables. ResultsAfter controlling for other variables, a 10% increase in real total revenue per hour worked was associated with 0.9%, 1.1%, and 1.6% decrease, respectively, in the incidence rates of all reported injuries, reported injuries with lost workdays, and the most serious injuries reported. ConclusionWe found an inverse relationship between profitability and each of the three indicators of occupational injuries we used. These results might be partially due to factors that affect both profitability and safety, such as management or engineering practices, and partially due to lower investments in safety by less profitable mines, which could imply that some financially stressed mines might be so focused on survival that they forgo investing in safety.

Effects of Great Barrier Reef degradation on recreational reef‐trip demand: a contingent behaviour approach*

There is a growing concern that increased nutrient and sediment runoff from river catchments are a potential source of coral reef degradation. Degradation of reefs may affect the number of tourists visiting the reef and, consequently, the economic sectors that rely on healthy reefs for their income generation. This study uses a contingent behaviour approach to estimate the effect of reef degradation on demand for recreational dive and snorkel trips, for a case study of the Great Barrier Reef in Australia. Results from a negative binomial random effects panel model show that the consumer surplus current reef visitors derive from a diving or snorkelling trip is approximately A$185 per trip. Furthermore, results indicate that reef trips by divers and snorkellers could go down by as much as 80 per cent given a hypothetical decrease in coral and fish biodiversity. This corresponds to a decrease in tourism expenditure by divers and snorkellers on full‐day reef trips in the Cairns management area of the Great Barrier Reef Marine Park of about A$103 million per year.

Fixed and Random Effects Models for Count Data

The most familiar fixed effects (FE) and random effects (RE) panel data treatments for count data were proposed by Hausman, Hall and Griliches (HHG) (1984). The Poisson FE model is particularly simple and is one of a small few known models in which the incidental parameters problem is, in fact, not a problem. The same is not true of the negative binomial (NB) model. Researchers are sometimes surprised to find that the HHG formulation of the FENB model allows an overall constant – a quirk that has also been documented elsewhere. We resolve the source of the ambiguity, and consider the difference between the HHG FENB model and a ‘true’ FENB model that appears in the familiar index function form. The familiar RE Poisson model using a log gamma heterogeneity term produces the NB model. The HHG RE NB model is also unlike what might seem the natural application in which the heterogeneity term appears as an additive common effect in the conditional mean. We consider the lognormal model as an alternative RENB model in which the common effect appears in a natural index function form.

Why is labour-managed firm entry so rare?: An analysis of UK manufacturing data

Why are there so few labour-managed entrants in market economies? We address this question by analysing a panel of labour-managed entrants into UK manufacturing industries. Using Poisson and negative binomial random effects models, our main finding is a significant negative relationship between entry counts and both the capital–labour ratio and the variance of profits. This is consistent with long-standing theoretical arguments that labour-managed firms face problems in raising capital and spreading risk.

Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections

Poisson and negative binomial (NB) models have been used to analyze traffic accident occurrence at intersections for several years. There are however, limitations in the use of such models. The Poisson model requires the variance-to-mean ratio of the accident data to be about 1. Both the Poisson and the NB models require the accident data to be uncorrelated in time. Due to unobserved heterogeneity and serial correlation in the accident data, both models seem to be inappropriate. A more suitable alternative is the random effect negative binomial (RENB) model, which by treating the data in a time-series cross-section panel, will be able to deal with the spatial and temporal effects in the data. This paper describes the use of RENB model to identify the elements that affect intersection safety. To establish the suitability of the model, several goodness-of-fit statistics are used. The model is then applied to investigate the relationship between accident occurrence and the geometric, traffic and control characteristics of signalized intersections in Singapore. The results showed that 11 variables significantly affected the safety at the intersections. The total approach volumes, the numbers of phases per cycle, the uncontrolled left-turn lane and the presence of a surveillance camera are among the variables that are the highly significant.

Evaluating Median Crossover Likelihoods with Clustered Accident Counts: An Empirical Inquiry Using the Random Effects Negative Binomial Model

Insights into plausible methodological frameworks specifically with respect to two key issues—(1) mathematical formulation of the underlying process affecting median crossover accidents and (2) the factors affecting median crossover frequencies in Washington State—are provided in this study. Random effects negative binomial (RENB) and the cross-sectional negative binomial (NB) models are examined. The specification comparisons indicate benefits from using the RENB model only when spatial and temporal effects are totally unobserved. When spatial and temporal effects are explicitly included, the NB model is statistically adequate, while the RENB model appears to lose its distributional advantage. Such findings might be artifacts of the median crossover accident dataset used in this study. While the NB model appears to be the superior model in the present case of median crossover accidents, the marginally inferior performance of the RENB model warrants further examination through application to regular accident types in light of its flexibility to incorporate temporal and cross-sectional variations simultaneously in panel counts. From a predictive standpoint, RENB offers advantages in terms of model transferability and updating.