Explaining the state-owned enterprise wage premium in China:is it unobserved heterogeneity?

State-owned enterprises(SOEs)in China pay higher wages thanprivate firms.Is it because SOEs pay their workers wage premiumor because they employ high-quality workforce?Using the latestmethods and data,this paper accounts for the unobserved heterogeneity and estimates the SOE wage premium for the period1995–2013.It is found that the wage premium has diminishedsince 1995 and has become insignificant since 2000.The significant wage gap between SOEs and non-SOEs can be explained bythe fact that SOEs recruit high-quality workforce in correlationwith SOEs’industrial composition.This paper also evaluates theinstruments used in the previous studies and rejects themthrough validity tests.The evidence suggests that the labour market in China is not segmented by ownership.

Comparison of Semi-Parametric Shared Frailty Models for Bees’Survival

Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.

Factors Explaining the Dynamics of Agricultural Technological Innovations Adoption: Evidence from Senegal’s Rain Maize Farmer

This paper analyzes the adoption dynamics of improved rainfed maize seeds disseminated in Senegal in 2013 by the West African Agricultural Productivity Program (WAAPP). We group maize producers into five groups (non-adopters, laggards/abandoners, late adopters, followers and pioneers/innovators) and take into account the heterogeneity of unobservable characteristics of the producers. In the pioneers/innovators group, the availability of labour, household size, shocks, and frequency of access to advice positively influence adoption, whereas financial constraints and high numbers of plots reduce the probability of adoption. Producers in the followers’ category tend to be older and more educated than those in the other categories. However, food insecurity and shocks such as diseases hamper adoption. For the group of late adopters, household size and available storage infrastructures explain adoption. However, the number of plots and shocks reduces their probability of adoption. Laggards tend to face shocks and food insecurity. The authors recommend to consider the dynamics of the adoption of technological innovations and heterogeneity of the characteristics of adopters groups in future research. They also recommend farmers to increase their adoption rate of the “Early Thai” and “Suwan 1” seed varieties thanks to their higher yields compared to traditional varieties. Also, a higher adoption rate would positively impact the food security of maize farmers in Eastern Senegal and High Casamance, especially in terms of availability. Other studies measuring the number of years needed for large-scale adoption of improved seed varieties should be conducted.

Exploring injury severity of children and adolescents involved in traffic crashes in Greece

For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece,disaggregate crash data including 13,431 involving children and adolescents from all regions of Greece for the period 2006–2015 were utilized.In order to identify factors affecting injury severity and account for potential unobserved heterogeneity,a series of mixed logit models were utilized.To explore and address potential temporal instability of crash-related risk factors,the likelihood ratio test was applied.Results indicated that night crashes,crashes outside urban areas as well as crashes involving bicycles or powered-two-wheelers are associated with higher injury severity of children and adolescents.Interestingly,crashes involving pedestrians are associated with lower injury severity than head-on collisions and run-off-road collisions with fixed objects.Side and sideswipe crashes also result in lower injury severities.The likelihood ratio test indicated that crash-related factors are instable when comparing the models utilizing data before and after 2010 respectively.This study contributes to the current knowledge in the field,as to the best of our knowledge this is the first study that addresses unobserved heterogeneity when analyzing child and adolescent injury severity.Overall,the findings of this study provide useful insights and could assist in unveiling crash risk factors and prioritize programs and measures to promote road safety of children and adolescents.

Impact of drivers'attributes on children injury severities in traffic crashes

Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries.In this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit model.As per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury risk.The child’s age was found to be inversely related to the risk of injury.Similarly,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes.When it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired drivers.Furthermore,improper driving actions,such as running off the road,raised the risk of incurring injuries to children.The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.

Exploring the stimulative effect on following drivers in a consecutive lane change using microscopic vehicle trajectory data

Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative effect on following drivers in a consecutive lanechanging scenario.The microscopic trajectory data from the HighD dataset are used for driving behaviour analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario,and not only distance-and speed-related factors but also driving behaviours are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver’s psychological heterogeneity in the consecutive lane-changing situation.Furthermore,a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision.Results indicate that 1)the consecutive lane-changing behaviours have a significant negative effect on the following lane-changing vehicles after lane change;2)the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers;and 3)the utility prediction model can be used to detect an improper lane-changing decision.