Binomial Logit Model Research Articles

Why are workers in the Spanish energy industry more likely to earn higher wages than other Spanish workers?: Wages in the Spanish energy sector

In Spain, 85.5 % of energy industry workers have a salary that is more than the Spanish average salary. We apply a binomial logit model based on the maximum likelihood estimation method to analyze the factors that determine the probability of obtaining these higher wages. The sample used was taken from the Wage Structure Survey (2018) prepared by the Spanish National Institute of Statistics. Firstly, the results show that this sector has a consolidated workforce and that the workers' educational levels are appropriate for their jobs. Secondly, there are no gender-based wage differences in this industry. Finally, working conditions and wages are established through collective bargaining agreements at the company level, which stands in contrast to other types of agreements established in the rest of the Spanish economy.

Kombinasi Model Trip Generation-Modal Split (G-MS) Dalam Perencanaan Bus Bandara Terintegrasi

The modal split model can be varied to other model in the concept of four step models transportation. This approach model can be varied depending on the purpose of transport planning, such as the modal split planning process which is carried out at the trip generation calculation stage, and in this process the movement of public transport can be directly separated from private transport with the assumption that someone who will travel can choose the model that is influenced by socioeconomic valiable. The model combined experiment (G-MS) was carried out on the route to and from Jambi’s Sultan Thaha Airport, which is one of the transportation nodes that plays an important role in regional development in Jambi City. The aim of this research is to analyze potential demand using a combination of the Trip Generation-Modal Split (G-MS) model. Based on the results of the analysis using the binomial ratio logit model with the value Y = Proportion of passengers/days. The passenger distribution area is devided base on zones where the zones that have the potential to be passenger gathering point are zone 9, zone 10 and zone 11. Calculation of the chi-square test (goodness of fit test) shows that the calculation X2 value is 11.583 and the table X2 is 14.067 because X2 count <X2 table, then H0 is accepted which means the observation results are the same as the average model results so that model validation is declared successful, so this research can be used to calculate passenger demand to plan airport buses.

The value of waiting spaces: Tourists’ willingness to pay for high-quality bus terminals

Transport quality has long been recognized as an important factor in influencing travelers’ behavior, and transport terminal quality undoubtedly plays no small part. Indeed, transit promotion policies explicitly based on qualitative factors and high-standard architecture are increasingly being adopted in designing new bus terminals and stops. High architectural standards can be found in several bus terminals worldwide. Nevertheless, the literature in transport sector has yet to explore the impact of bus terminal “hedonic quality” on users’ behavior or use their willingness to pay (WTP) in cost–benefit analyses or other transport policy applications. Hedonic value is here intended as the aggregate of all elements related to travelers’ pleasure in spending time in a terminal where architectural beauty and the passenger services offered are arguably the most visible and representative attributes. Within this context, we propose a quantitative analysis of the perceived hedonic value of bus nodes in terms of users’ WTP (pure preference) for a high-quality bus terminal. To this end a discrete choice experiment based on a visual immersive experience was performed, and data were collected from 324 residents of Milan (Italy) traveling for tourism. Different binomial Mixed Logit models with panel data and random coefficients were estimated for the purpose. Monetary valuation of pure preference for using a high-quality bus terminal was estimated with a mean of about 25 % of the average trip cost observed for the case study considered. This pure preference means that the Italian tourist is willing to spend up to €4.35/trip more for a high-quality bus waiting space or travel up to 28.2 min/trip more, instead of using a traditional bus terminal for the same trip. Category-specific analyses show that females > 30 years old have a greater pure preference for a high-quality bus terminal (up to + 220 %) against males and young users. The employed also have a higher (+42 %) pure preference for the beauty of a bus terminal against others. The results of this paper should be compared with those from other case studies as they have a potential impact for transportation planning applications, underling the importance of also incorporating “hedonic quality” as explicit design variables for new/revamped transportation hub. At the same time, new challenges are posed for modeling user behavior and determining quality-related indicators and measures.

Exploring business and SDG discourse on X: topics, users and engagement

PurposeThis paper examines thematic discourses concerning business and the Sustainable Development Goals (SDGs) on X (formerly Twitter), aiming to uncover active user groups and evaluate engagement levels across various topics. The study also explores the engagement patterns among different user categories, ultimately seeking deeper insights into platform discourse regarding business and the SDGs.Design/methodology/approachUtilizing unsupervised machine learning technique Latent Dirichlet Allocation (LDA), we perform exploratory topic modeling on X data referencing business and the SDGs, generating 16 thematic clusters. Subsequently, we analyze user descriptions to categorize users involved in these discussions. Finally, we employ binomial logit models to assess the relationship between topics and engagement and chi-squared test to evaluate the relationship between users and topics.FindingsThe exploratory research identifies 16 business and SDG topics, while the analysis of users reveals 6 stakeholder groups contributing to these discussions. Business groups emerge as the most frequent contributors, posting on topics related to partnership, action advocacy, and economic outcomes. Topics about updates on progress and transformative initiatives garnered strongest support for engagement.Originality/valueThis research not only sheds light on the current state of business and SDG discourse on X, but also underscores the significance of engaging external stakeholders in driving positive social change globally.

Heterogeneous impacts of the built environment on survival of customized bus services across the downtown and suburbs

Customized bus services (CBS) lines have been increasingly added in cities to better serve diversified travel needs. The notable variations in the built environment across the downtown and suburbs could result in wide differences in local customers' behaviors and preferences. The allocation of CBS stops thus needs to be considered separately in urban centers and peripheries to ensure the best performance, but such spatial heterogeneity is not specifically studied and justified in the literature. This study takes empirical data containing continuous CBS stops in Shanghai, China, as an example. Using a latent class binomial logit model, it investigates the effects of bus characteristics, geographic, and built environment elements on the survival of CBS stops in downtown and suburbs. The findings demonstrate that the performance of CBS systems is significantly influenced by factors including road density, population density, POI, sidewalk width, greenery area, ticket fare, and the type of CBS line (commute or general) across urban centers and peripheries. Specifically, two latent classes of CBS stops are found to have different effects depending on the stop's location or type of the linked areas, i.e. downtown or suburbs, on a CBS line.

Inheriting housing inequality: Parental homeownership and place of origin as key factors for homeownership in Japanese metropolitan areas

The term “housing ladder” refers to the phenomenon of people moving from one housing type to another according to changing demands of different life stages. This phenomenon is observed in many countries and is known as the Jutaku Sugoroku Paradigm in the Japanese context. This study aims to clarify whether adult children's access to homeownership might be affected by their parents' homeownership and their place of origin. Questionnaire Data from a survey across Japan's two largest metropolitan areas, Tokyo and Osaka, were analyzed by combining crosstabulations and a binomial logit model. The results revealed that individuals are more likely to attain homeownership if they come from a metropolitan area and if their home of origin is an owner-occupied house. These findings suggest that housing inequality is being passed on to the next generation by parents' homeownership status and residential location. Japanese housing policies that promote private ownership of housing may reinforce these inequalities. The study concludes that Japan experiences the same intergenerational transmission of housing inequality due to homeownership as other countries, whereas place of origin was an additional factor for homeownership in the Japanese context.

Determining factors in the default of Agroamigo beneficiaries in the Sobral/CE region

The aim was to investigate the default of Agroamigo beneficiaries in the Sobral/CE region. To this end, a binomial logit model was used to measure the impact of certain categories on the probability of default, and 200 questionnaires were administered to producers benefiting from the program. After analyzing the results, it was concluded that the only activity that contributed to reducing default was goat farming.

Flexibilization or biomethane upgrading? Investment preference of German biogas plant operators for the follow‐up of guaranteed feed‐in tariffs

AbstractThis article reports the results of a discrete choice experiment with 183 German biogas plant operators designed to elicit the respondents' plans for biogas utilization pathways after the end of guaranteed feed‐in tariffs. Participants could choose between ‘flexibilization’ for demand‐based electricity generation and conversion to biomethane upgrading for direct feed‐in into the natural gas grid. A binomial logit model revealed a 37% probability of switching to biomethane upgrading. These plants are characterized by higher capacities, several involved shareholders, secured succession, costly digestate disposal and belonging to the upper performance quartile. Mixed logit estimations conducted separately for the two investment concepts revealed a very high overall willingness to invest: 71% for flexibilization and 82% for biomethane upgrading. The respondents demand a return on investment of 19% for flexibilization and 26% for biomethane upgrading. Within the flexibilization, twofold overbuilding (installed capacity equals 2 times the rated power) is clearly preferred to fivefold overbuilding. For the biomethane upgrading, private ownership of the upgrading plant is preferred to a joint investment in a central upgrading facility. Limiting the use of energy crops reduces the propensity to invest in both models, while a longer utilization period enhances it. The respondents consider lack of planning reliability as the biggest obstacle to invest, followed by long approval procedures and high investment costs due to restrictive legal requirements.

Bacterial wilt and dissemination factors in tomato production areas of Benin, West Africa

Bacterial wilt caused by Ralstonia solanacearum is a major constraint for tomato production in Benin. Through this research, the distribution and incidence of bacterial wilt and its dissemination factors in tomato fields were assessed. Identification of isolates were based on morphological characterization of colonies, hypersensitivity, and pathogenicity tests. Dissemination factors were analysed through a binomial logit model. Bacterial wilt was widespread in all Benin’s eight Agro-Ecological Zones (AEZs), and mostly severe in AEZs dominated by ferralitic soils where its highest incidence was up to 100%. Other variables such as post-harvest crop residues plowed into soils, rotation practices, use of mineral fertilizer, watering practices, use of the same tools in many fields, positively influenced the probability of bacterial wilt infection in tomato fields. Dissemination of R. solanacearum is more favorited by women agricultural practices than men. Farmer’s awareness on good agricultural practices could help to reduce the pressure of the disease.

Analysis of entrepreneurs’ motivations and role models for growth expectations in the time of coronavirus

The change in life and professional conditions in 2020 has impacted the growth expectations of new firms. Based on a sample of more than 20,000 people coming from the Spanish Global Entrepreneurship Monitor (GEM) for 2019 and 2020, we analyze the effect of entrepreneurs’ motivations and social role models on the growth expectations of new firms using a binomial logit model. The results show that, as a consequence of the institutional conditions caused by the COVID-19 pandemic, entrepreneurs only take into consideration the economic projections of the new firm in order to take the risk of hiring employees in the early stage of the firm’s creation. However, experienced entrepreneurs involved in the consolidating stage of the entrepreneurial process have more ambitious motivations, and take advantage of their knowledge derived from peer role models in order to face the new opportunities arising during the pandemic. The effect of motivations and role models on firms’ growth also differ between men and women.

IMPACT OF THE JAKARTA - SURABAYA HIGH - SPEED TRAIN ON PASSENGERS OF THE JAKARTA - SURABAYA EXECUTIVE TRAIN CORRIDOR

Due to the increasing demand for travel by train in recent years, the large potential number of passengers in Jakarta - Surabaya corridor needs to be anticipated with adequate transportation system planning so that the bottlenecks on available transportation network system can be minimized. This paper analyses the potential competition between the demand of executive passenger train and Jakarta-Surabaya high-speed train which will be built by the Government as planned. This study only use travel time variables and ticket rates as independent variables between executive passenger train and high-speed train, then stated preference methods and binomial logit models are used as tools for processing data. As a result, we believe that the operation of Jakarta-Surabaya high-speed train will have a negative impact on the demand of executive passenger train on this route.

Understanding German farmers’ intention to adopt drought insurance

Climate risks, particularly droughts and heat waves, negatively affect agricultural incomes worldwide. Drought insurance is promising to mitigate resulting income losses at the farm level. As the proportion of German farmers insured against drought is low, policymakers and insurers aim to increase the appeal of drought insurance to farmers. However, to accelerate their efforts in this regard, more information regarding farmers' intention to adopt drought insurance beyond current adoption is needed. To obtain initial insights, we surveyed 127 German farmers in a risk management context and applied a modified transtheoretical model of behavioral change. This revealed detailed information on the gradual adoption of drought insurance. Given a heterogenous distribution among the gradual stages of adoption, a binomial logit model was estimated instead of an ordered logit to investigate farmers' current intention to adopt drought insurance. Furthermore, the machine learning technique of least absolute shrinkage and selection operator (LASSO) was applied to select the most relevant features to be used as explanatory variables in the estimation. The results show that farmers' gender and risk attitude, land tenure, how severely they were affected previously by weather risks, and the level of trust in index insurance products have a statistically significant effect on farmers' intention to adopt drought insurance. Additionally, this study provides insights into farmers' reasons against drought insurance. As such, the results are important to policymakers considering policy intervention, insurers interested in farmers' intention to insure and to researchers focusing on farmers’ adaptation to climate change.

More travel, more well-being of older adults? A longitudinal cohort study in China

ObjectivesThe aging of the Chinese population constitutes a challenge to improving the well-being of older adults. However, well-being is an iterative and temporal process with age. The main objective is to analyze the influence of travel behavior and socio-demographic characteristics on fluctuating well-being of older adults among generations. MethodsUsing Chinese General Social Survey (CGSS) data (2011–2018), cohort analysis and binomial logit model were applied to conduct longitudinal studies on the association between well-being and travel behavior of older adults. ResultsThe travel frequency for social and exercise significantly impacted the older adults’ well-being, while the extent of the effect of shopping trips decreased over time. A notable finding is that travel frequency for shopping had a significantly positive impact on the well-being of older adults over 75, as opposed to exercise trips. Moreover, it is surprising that vehicle ownership has a significant influence on the well-being of the older adults in Pre-old, Tra-old, and You-old (cohorts 1, 2, and 3) aged below 70, while no significant impact in Mid-old, Hig-old, and Lon-old (cohort 4, 5, and 6). ConclusionsThe study considered the changes in the well-being of older adults as they age. Meanwhile, the effects of cohort differences in travel behavior on well-being were also highlighted in the paper. The extent to which the frequency of shopping trips affected well-being decreased over time, and there has been no significant effect in recent years. Therefore, emphasis should be placed on social and exercise trips compared with shopping trips. For example, we suggested improving the transportation infrastructure around parks and squares, such as adding special bus lines. The study could help policymakers offer targeted travel services to safeguard older adults’ well-being.

Corruption, market structure, and industry competition in the Vietnamese construction sector

The Vietnamese construction sector is arguably the country’s industry most vulnerable to corruption, leading to poor functioning of the sector. This analysis empirically investigates how corruption affects competition in the construction industry, and how different market structures shape different types of corruption and its potentially different impacts. Our empirical models for industry competition are estimated on a panel of 33,045 year-observations using the Fractional Binomial Logit model. The findings demonstrate that corruption reduces competition, and that the effects of corruption depend on the type of corruption and market structure in which firms operate. We find evidence that corruption is characterized by collusion between local authorities and dominant firms in highly concentrated markets, leading to the persistent concentration of the sector in highly corrupt areas. In areas with lower corruption, corruption is characterized by bureaucratic holdups while lowered corruption increases industry competition. Petty corruption is observed in less corrupt areas while it does not affect industry competition. The finding suggests that promoting transparency in public procurement regulatory processes enhances industry competition and mitigates corruption risks associated with different market structures. Additionally, implementing reforms to ensure equitable access to reliable public reporting and services can facilitate a more concentrated yet efficient industry structure.

Car Use: A Matter of Dependency or Choice? The Case of Commuting in Noord-Brabant

Car use in the sprawled urban region of Noord-Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High-Tech Campus (2018) and the TU/e-campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car-dependent. Of the non-car-dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provision) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.

Travel Route Selection Model After the Operation of the Semarang-Demak Toll Road

The highway is an infrastructure that is very supportive for the movement of community activities. So that the government develops a road network specifically in the Java area in the form of the Semarang-Demak toll road construction. The purpose of this study is to obtain a route choice model that can explain the probability of road users choosing toll roads, and to estimate the sensitivity of route choice if there is a change in its attributes. The stated preference questionnaire was used as a medium to obtain primary data. This study uses two variables: the difference in toll rates and the difference in travel time. The survey results were processed using the binomial logit model. Regression analysis using SPSS program to obtain the utility function. The model results obtained are Utol – Unontol = -5.813 - 0.000727(X1) + 0.251(X2). The results of the utility calculation are used to obtain the probability value of choosing a travel route. If the toll rate remains or does not change, the probability of respondents to use the toll road will be greater. On the other hand, if there is an increase in toll rates, respondents tend to choose to use the national road

Effectiveness of Travel Demand Management Policies in Promoting Rail Transit Use and Reducing Private Vehicle Emissions: A Stated Preference Study of Bangkok, Thailand

In this study, we focused on policies to promote reduction in the use of private vehicles that could be implemented in Bangkok during periods of severe PM2.5 levels, including a flat charge for use of private cars, private vehicle bans, and public transport fare subsidization. The objective was to investigate how these policies can be used to help convince private car users to shift their travel modes to rail transit, and, thus, reduce vehicle emissions that contribute to air pollution. We conducted a stated preference survey of 731 private car users in Bangkok, Thailand, where stated-choice scenarios were specified with respect to all possible combinations of the proposed policies that could lead to a reduction of PM2.5 concentration. A binomial logit model was used for the analysis. Our results suggest that the effectiveness of the travel demand management policies would vary across geographical areas of the city. Public transport fare subsidization would be effective in reducing car use among residents in the inner suburbs. A car ban would be an effective measure in outer suburbs, while the flat charge would be effective among populations in central Bangkok and the inner suburbs.

Analysis of the selection of land transportation modes in Serang City

The role of urban transportation is vast in supporting the mobility of city residents because, after all, urban people need city transportation, and most community groups depend on city transportation to meet their mobility needs. Along with the development of the era of online application-based transit, they are becoming newcomers as a service that makes it easier for people to carry out travel mobility, such as online motorcycle taxis. This study aims to determine the mode selection model between city transportation and online motorcycle in Serang City. The research approach is quantitative, with primary data sources obtained from distributing closed questionnaires to 43 city transportation travelers and 57 respondents to online motorcycle drivers. The data analysis used is the difference binomial logit model to determine the probability model of the traveler in choosing the mode. The results of modeling the choice of the way with the difference in binomial logit results in city transportation having the opportunity/probability of being chosen by travelers in Serang City at 45.64% while the probability/chance of selecting an online motorcycle is 54.36%.

Changes in credit score, transaction volume, customer characteristics, and the probability of detecting suspicious transactions

PurposeThis study aims to use a unique customer-information data set from a Norwegian bank to identify how small changes in firm-specific factors correlate with the risk of a client subsequently being involved in suspicious transactions. It provides insight into the importance of updating client risk based on changes in transaction volume and credit risk to enable effective resource use in transaction monitoring.Design/methodology/approachChanges in a firm’s bank use and accounting data were tested against subsequent flagged and reported customers to identify which changes led to a significant increase in the probability of engaging in a transaction identified as suspicious. Prioritizing resources to firms that remain suspicious after further controls can improve the risk-based approach and prioritize detection efforts. The main factors were customer probability of default (credit score), size and changes in customer characteristics. The cross-sectional data set contained administrative data on 8,538 corporate customers (219 with suspicious transactions that were subsequently flagged, 64 of which were reported). A binomial logit model was used.FindingsChanges in transaction volume and bank use are significant in predicting subsequent suspicious transactions. Customer credit score changes were significantly positively correlated with the likelihood of flagging and reporting. Change is a stronger indicator of suspicious transactions than the level. Thus, frequent updating of client risk and using a scale rather than risk categories can improve client risk monitoring. The results also showed that the current anti-money laundering (AML) system is size-dependent; the greater the change in customer size, the greater the probability of the firm subsequently engaging in a suspicious transaction.Research limitations/implicationsClient risk classification, monitoring changes in a client’s use of the bank and business risk should receive more attention.Practical implicationsThe authors demonstrate that client risk classifications should be dynamic and sensitive to even small changes, including monitoring the client’s credit risk changes.Social implicationsDirecting AML efforts to clients with characteristics indicating risk and monitoring changes in factors contributing to risk can increase efficiency in detecting money laundering.Originality/valueTo the best of the authors’ knowledge, this is the first study to focus on changes in a firm's use of a bank and link this to the probability of detecting a suspicious transaction.

Factors Affecting Residential Real Estate Investments of U.S. Individuals

This paper aims to study the causal relationships between earnings, age, number of kids, and education level of U.S. individuals in residential real estate investments. This empirical study analyzes a total of 421,911 observations based on the interview of indi- viduals from the Survey of Income and Program Participation (SIPP). By employing the econometric techniques of binomial logit and probit models, multinomial logit model, and Tobit model on censored data, the results showed that most individuals in the U.S. are willing to invest in real estate. Interestingly, as the education level increases, people tend not to invest in real estate. However, with the increase in earnings, the number of kids, and age, people are more likely to invest in real estate.