Role of safety in declining public transport use: Empirical evidence from India

PurposeUsing primary survey data from an urban area in Global North, this study aims to examine the impact of sociodemographic factors on perception of usefulness of public transport and the importance of safety in preferring private modes of transport over public.Design/methodology/approachThe study uses stratified random sampling technique to collect data on travel behavior and socioeconomic characteristics. Descriptive statistics complemented with bivariate probit model and seemingly unrelated bivariate probit model is implemented on the data obtained.FindingsThe study finds that women, unmarried individuals, the youngest age group, least educated individuals and those who are working are expected to finding public transport more useful as compared to their respective counterparts. Despite finding the mode most useful, women are more likely to find it unsafe to travel.Research limitations/implicationsThe study calls attention to not only dealing with the infrastructural changes in system but also with those attached insecurities which limit its use.Originality/valueTo the best of our knowledge, this is the first time a comprehensive evaluation of the demands and challenges for transportation services faced by different segments of the society is carried out in this section of the developing world.

Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico

We analyse the performance improvement on a monetary policy model of introducing non-conventional market attention (NCMA) indices generated using big data. To address this aim, we extracted top keywords by text mining Banco de Mexico’s minutes. Then, we used Google search information according to the top keywords and related queries to generate NCMA indices. Finally, we introduce as covariates the NCMA indices into a bivariate probit model of monetary policy and contrast several specifications to examine the improvement in the model estimates. Our results show evidence of the statistical significance of the NCMA indices where the expanded model performed better than models only including conventional economic and financial variables.

A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria

Stunting and wasting are major malnutrition issues among children under five years of age and have continued to remain unacceptably high in Nigeria leading to high rates of child morbidity and mortality. Evidence-based strategies are required by government and non-governmental agencies to mitigate the suffering of these children, and this could be realised when the association between the determinants and the geographical distributions are fully understood. Using data from four waves of the Nigerian Demographic and Health Survey, we employed a distributional bivariate probit model to examine the geographical distributions of the levels and linear association between acute and chronic malnutrition in Nigeria after accounting for possible observed determinants. Bayesian inference was based on Markov chain Monte Carlo simulation. The findings reveal substantial spatial variations in stunting and wasting among under-five children in Nigeria, indicating a north–south divide. The findings show negative linear association between the two malnutrition indicators among children in some northern fringe states but positive for Akwa Ibom, Ebonyi and Anambra. The correlation also peaks around age 20 months indicating that during the first 2 years of life, the children have an increasing likelihood of suffering from stunting and wasting.

Energy poverty and its relationship with health: empirical evidence on the dynamics of energy poverty and poor health in Australia

Does poor health increase the likelihood of energy poverty or vice versa creating a vicious poverty trap? We use data from the Household, Income and Labour Dynamics of Australia (HILDA) survey from 2005–2018 to explore if these two processes are dynamically related across a number of subjective and objective measures of physical and mental health as well as subjective and objective measures of energy poverty. We employ univariate dynamic models, introduce controls for initial conditions, and explore inter-dependence between energy poverty and health using a dynamic bivariate probit model. Our results show that controlling for initial conditions impacts on the magnitude and significance of the lagged coefficients. We only find cross-dependency effects between energy poverty and health for subjective measures of energy poverty. This suggests that individuals’ feelings about being in energy poverty may impact on their health leading to poor health/energy poverty traps. Targeting individuals in financial stress/debt may be one way to reduce these poor health/energy poverty traps.

Village Environment, Capital Endowment, and Farmers’ Participation in E-Commerce Sales Behavior: A Demand Observable Bivariate Probit Model Approach

With the recent developments in widespread internet usage and digital technology, an ultimate worldwide transformation in information and communications technology has occurred. Especially, how people engage in the virtual market for buying and selling goods has changed dramatically, which flourished the playground of electronic commerce (EC). Interestingly, it has become crucial to create an ample opportunity for farmers to utilize a more comprehensive market range for selling their products. However, farmers participating in e-commerce sales platforms may be interrupted by various internal and external factors. Therefore, the study’s primary goal is to evaluate the impacts of various external and internal factors on shaping farmers’ behavior in participating in e-commerce sales platforms. The study utilized a demand observable bivariate Probit model to analyze the village environment and capital endowment effects to craft the findings. The study utilized micro-survey data from 686 households in the leading kiwifruit-producing area as the empirical setup. The findings illustrated that the village environment is the main factor that restricts farmers’ e-commerce sales behavior, among which the infrastructure and policy environments have a significant contribution to farmers’ e-commerce sales intention and behavior. However, the effect of capital endowment on farmers’ e-commerce selling behavior has been found as significant. The village environment significantly affects both large- and small-scale farmers, and the capital endowment has a higher binding effect on small-scale farmers. Therefore, the paper suggests that improving the village environment for e-commerce development and laying the foundation for e-commerce development should be fostered. A differentiated incentive mechanism to improve the capital endowment of farmers should be constructed. A well-structured capital endowment triggering small farmers to capture the benefits of e-commerce sales should be imposed. The government should extend the support of the agricultural demonstration zone to facilitate practical training among the smallholder farmers. The formal and informal risk-sharing and financial institutions should prioritize building infrastructure to support farmers’ short- and long-term investments. Farmers should realize the importance of e-commerce for integrating the agricultural value chain.

Analysis of Trends and Correlation in Child Restraint Use and Seating Position of Child Passengers in Motor Vehicles: Application of a Bivariate Probit Model

Motor vehicle crashes are a leading cause of death and injury for children under 8 years. While different states are showing increases in the proportion of child restraint device use, only around half of the children aged between 4 to 7 years are being properly restrained. This study was undertaken to identify the factors contributing to the proper child restraint use and child passenger’s seating position through the direct observation surveys of more than 10,000 child passengers in 2015 and 2018 in Michigan. A bivariate probit model was developed to simultaneously identify the contributing factors for the proper restraint use and seating position of child passengers. The bivariate framework is able to account for correlation of the two dependent variables in the study. The results show that the two dependent variables are positively correlated, and this correlation is strongly significant. Also, the key factors simultaneously influencing proper child restraint use and appropriate seating position of the child passenger include the age of the child, number of the child passengers in the vehicle, driver belt use, driver gender, age, and race, vehicle type, stratum, weather, and the time of the day and week. However, factors such as county-specific population, income, and education, and the type of location did not have a significant association with either child restraint use or the seating position of the child passenger.

The Effects of Openness on Managerial Innovation in Cameroonian Companies

The objective of this study was to analyze the effects of openness on the adoption of managerial innovation by Cameroonian companies, as well as comparing the share of managerial innovation resulting from inter-organizational networks of the same group and of different groups. Noting a lack of such a study on Cameroon, this study used data from the Centre de Recherche en Economie et Gestion (CEREG) to achieve the objective. Using a binary probit model and a recursive bivariate probit model, the authors found that, first, a company that collaborates with other companies has an increased probability of 0.37 of adopting new managerial practices, compared to another company that does not collaborate. Second, a company belonging to a group that collaborates with companies of a different group has an increased probability of 0.30 of adopting new managerial practices, compared to a company that only collaborates with companies of the group to which she belongs. Business leaders should cooperate with all market players.

Seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia: a population-based study

Properly conducted serological survey can help determine infection disease true spread. This study aims to estimate the seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia accounting for non-response bias. A sample of adults was recruited with random digit dialling, interviewed and invited for anti-SARS-CoV-2 antibodies. The seroprevalence was corrected with the aid of the bivariate probit model that jointly estimated individual propensity to agree to participate in the survey and seropositivity. 66,250 individuals were contacted, 6,440 adults agreed to be interviewed and blood samples were obtained from 1,038 participants between May 27 and June 26, 2020. Naïve seroprevalence corrected for test characteristics was 9.0% (7.2–10.8) by CMIA and 10.5% (8.6–12.4) by ELISA. Correction for non-response decreased estimates to 7.4% (5.7–9.2) and 9.1% (7.2–10.9) for CMIA and ELISA, respectively. The most pronounced decrease in bias-corrected seroprevalence was attributed to the history of any illnesses in the past 3 months and COVID-19 testing. Seroconversion was negatively associated with smoking status, self-reported history of allergies and changes in hand-washing habits. These results suggest that even low estimates of seroprevalence can be an overestimation. Serosurvey design should attempt to identify characteristics that are associated both with participation and seropositivity.