Binomial Logit Model Research Articles

Characteristics of Unbanked Households in the Republic of Congo

The objective of this article is to highlight the characteristics of unbanked households in the Republic of Congo. Starting from the Binomial Logit model based on data from the survey conducted by the National Credit Council (2015), the results show that age, gender, marital status, average income, holding an account with mobile money services, socio-professional category, type of employer are categories whose characteristics help define the profile of unbanked households in the Republic of Congo. Based on the results obtained, we can define the profile of an unbanked household in the Republic of Congo as follows: the unbanked Congolese household is most likely female, under 35 years of age, unmarried, does not have an account with mobile money services, is not a manager, works in the public sector and has an average income of less than 150,000 CFA francs. We recommend the public authorities to expand the device "payment of wages by bank account" to the private sector.

Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment

The present study reports on the preferences among e-bike users for commuting trips. An online survey with sixteen choice-based conjoint questions was conducted in March 2020 amongst 144 e-cyclists and non e-cyclists in Flanders (Belgium). Their choices were analysed using a no-choice binomial logit model. The impact of the following factors showed to be most important: weather conditions, trip time, type of e-bike, and financial support by the employer. The effect on e-bike users of factors such as cycling infrastructure, secure parking, and shower facilities at the workplace seems more limited. Based on the difference between male and female respondents, trip time and type of e-bike differed significantly. The only significant difference between e-bike owners and non e-bike owners was the type of e-bike. Trip time, weather conditions and financial intervention differed significantly between the young and old age group. Our findings suggest that policymakers should focus on investments in e-bikes, such as cycling infrastructure, mileage allowance and facilities at the workplace to make this a sustainable and socially inclusive mode of transport.

Consumer perceived ethicality of banks in the era of digitalisation: The case of Poland

Ethically conducted business has a positive impact on consumer trust, satisfaction and loyalty. The aim of this paper is to identify the bank activities that customers perceive as ethical and to what extent they affect consumer perceived ethicality. The binomial logit model applied in this study demonstrates that the honesty of financial advice, use of e-banking services and use of artificial intelligence technology to improve the quality of banking services have a significantly positive impact on consumers’ perception of banks in Poland as ethical institutions. Significant variables that negatively influence consumer perceived ethicality include gender, education, use of financial advisory services, banks’ manipulation of information about financial products and use of personal data against the will of customers.

Predictions from an Empirical Study in the Turkish Mobile Telecommunications Market on the Determinants of Mobile Customer Churn

Acquiring new customers compared to holding the existing ones is costlier and more troublesome for businesses, so customer retention is of great importance in today's intensely competitive environment. It is crucial in customer relations management to identify and analyse customers whose loyalty decreases and who tend to leave, and prevent churn through various methods under resource constraints. This issue is especially prominent in the mobile communication market. This paper uses a binomial logit model based on a survey with 637 mobile users in Turkey to determine the factors affecting customer churn and how they find their effect. Results indicate that, among various factors, network quality, billing, tariff level, tariff plan, and education level are the determinants affecting customer churn and associated with the intention to switch. Our findings demonstrate implications for both managers and rule-makers in the mobile telecommunications sector.

Elevated serious psychological distress, economic disruption, and the COVID-19 pandemic in the nonmetropolitan American West

In this study we examined the psychological distress, self-rated health, COVID-19 exposure, and economic disruption of a sample of the nonmetropolitan western U.S. population and labor force one year after the start of the COVID-19 pandemic. Using novel primary survey data from non-metropolitan counties in the eleven contiguous western United States collected from February 28 until April 3, 2021 (n = 1203), we descriptively analyzed variables and estimated binomial and multinomial logit models of the association between economic disruption, COVID-19 exposure, self-rated health, and psychological distress. Results showed there was widespread presence of psychological distress, COVID-19 exposure, and economic disruption among the overall sample and members of the labor force. There was extremely high incidence of serious psychological distress (14.8% CI [12.1,17.8] of the weighted sample), which was heightened among the labor force (16.6%, CI [13.0,20.9] of those in the labor force). We found economic disruption was associated with severe psychological distress, but exposure to infection was not. Comparatively, overall self-rated health was at similar levels as prior research and was not significantly associated with economic disruption or COVID-19 exposure. COVID-19, particularly its associated economic effects, had a significant relationship with serious psychological distress in this sample of adults in the nonmetropolitan western United States.

Profit-maximizing parcel locker location problem under threshold Luce model

The growth of e-commerce has created increasing complexity in logistics services. To remain competitive, logistics and e-commerce companies are exploring new modes as supplements to traditional home delivery, one of which is the self-service parcel locker. This paper studies a parcel locker location problem where a company plans to introduce the locker service by locating locker facilities to attract customers. The objective is to maximize the profit, accounting for the revenue and the cost of facilities, under the competition of other delivery modes. To estimate the revenue, we use the threshold Luce model (TLM) to predict customers’ likelihood of using the locker service. We then propose a combinatorial optimization model and develop exact solution methodologies that are practically implementable according to our extensive computational experiments. In effect, our modeling framework generalizes the traditional facility location problems based on the binomial logit model (BNL) and the multinomial logit model (MNL), both of which impose strong and strict assumptions on the customer’s choice sets. That is, they assume that the choice set will either contain only one facility or all facilities. In our numerical experiment, we demonstrate that using the BNL and the MNL could lead to, respectively, pessimistic and optimistic revenue estimation. Consequently, the suggested location decisions will be either conservative or aggressive. Our proposed model, by contrast, can effectively relax these assumptions. Our results also reveal that the aggressive decision due to the use of the MNL will incur an unnecessarily high facility cost that cannot be compensated by the additional revenue, leading to profit loss that could be significant in various scenarios. Finally, we conduct sensitivity analysis to shed light on how the relative attraction of locker services (compared to other delivery options) affects the location decisions and highlight the importance of calibrating the input parameters in TLM.

Immunothrombosis Biomarkers for Distinguishing Coronavirus Disease 2019 Patients From Noncoronavirus Disease Septic Patients With Pneumonia and for Predicting ICU Mortality.

Coronavirus disease 2019 patients have an increased risk of thrombotic complications that may reflect immunothrombosis, a process characterized by blood clotting, endothelial dysfunction, and the release of neutrophil extracellular traps. To date, few studies have investigated longitudinal changes in immunothrombosis biomarkers in these patients. Furthermore, how these longitudinal changes differ between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia are unknown. In this pilot observational study, we investigated the utility of immunothrombosis biomarkers for distinguishing between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia. We also evaluated the utility of the biomarkers for predicting ICU mortality in these patients. The participants were ICU patients with coronavirus disease 2019 (n = 14), noncoronavirus disease septic patients with pneumonia (n = 19), and healthy age-matched controls (n = 14). Nine biomarkers were measured from plasma samples (on days 1, 2, 4, 7, 10, and/or 14). Analysis was based on binomial logit models and receiver operating characteristic analyses. Cell-free DNA, d-dimer, soluble endothelial protein C receptor, protein C, soluble thrombomodulin, fibrinogen, citrullinated histones, and thrombin-antithrombin complexes have significant powers for distinguishing coronavirus disease 2019 patients from healthy individuals. In comparison, fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have significant powers for distinguishing coronavirus disease 2019 from pneumonia patients. The predictors of ICU mortality differ between the two patient groups: soluble thrombomodulin and citrullinated histones for coronavirus disease 2019 patients, and protein C and cell-free DNA or fibrinogen for pneumonia patients. In both patient groups, the most recent biomarker values have stronger prognostic value than their ICU day 1 values. Fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have utility for distinguishing coronavirus disease 2019 patients from noncoronavirus disease septic patients with pneumonia. The most important predictors of ICU mortality are soluble thrombomodulin/citrullinated histones for coronavirus disease 2019 patients, and protein C/cell-free DNA for noncoronavirus disease pneumonia patients. This hypothesis-generating study suggests that the pathophysiology of immunothrombosis differs between the two patient groups.

Product-specific determinants of Mobile Money Adoption: Evidence from Egypt during COVID-19

The paper aims to examine the factors affecting users’ adoption intentions of mobile money in Egypt during the COVID -19 using a binomial logit model. Our econometric estimation results show that, in general, perceived benefits, ease of use, and proximity of points of sale are the significant variables that specify the adoption of mobile money in Egypt. Moreover, socio-demographic characteristics showed different results. Women are more concerned about the perceived benefits and ease of use while men added other concerns: the time taken to conduct a transaction and the proximity to sale points. For individuals with lower income, ease of use is the primary determinant, while for those with higher income, it is the proximity of points of sale and safety, which determines the adoption of mobile money services. Finally, for young individuals, under the age of 30, perceived benefits, ease of use, and the proximity of points of sale are the main determinants of mobile money service use.

Mode Choice Analysis of Sustainable Public Transport in Bogor City of Indonesia

Public transport service scenarios must be well designed for public transport performance to be better and more sustainable. The aim of this study was to determine the percentage of the travelers move to public transportation in the Bogor City. The Stated Preference technique is used to know travelers responses to public transport service scenarios in utility functions. Furthermore, from the binomial logit model analysis obtained the probability of choice of public transportation mode. The results of the analysis have shown that the travelers responded positively to move to public transportation modes. In Corridor III, private car users and online-based motorcycle transport users choose the public transportation of non-economic medium bus type (scenario-2) with the greatest probabilities of 35.59 % and 61.02 %. In Corridor II, private car users choose the public transportation of non-economic big bus type (scenario-3) with the greatest probability of 36.31 %. Motorcycle users and online-based motorcycle transport users choose the public transportation of non-economic small bus type (scenario-1) with the greatest probabilities of 66.47 % and 70.64 %. The motorcycle users in Corridor II choose the public transportation in all scenarios with significant probabilities ranging from 62.03% to 66.47%. Keywords: mode choice analysis, public transportation, sustainable DOI: 10.7176/JESD/12-18-05 Publication date: September 30 th 2021

Applying the Social Vulnerability Index as a Leading Indicator to Protect Fire-Based Emergency Medical Service Responders' Health.

During emergencies, areas with higher social vulnerability experience an increased risk for negative health outcomes. However, research has not extrapolated this concept to understand how the workers who respond to these areas may be affected. Researchers from the National Institute for Occupational Safety and Health (NIOSH) merged approximately 160,000 emergency response calls received from three fire departments during the COVID-19 pandemic with the CDC’s publicly available Social Vulnerability Index (SVI) to examine the utility of SVI as a leading indicator of occupational health and safety risks. Multiple regressions, binomial logit models, and relative weights analyses were used to answer the research questions. Researchers found that higher social vulnerability on household composition, minority/language, and housing/transportation increase the risk of first responders’ exposure to SARS-CoV-2. Higher socioeconomic, household, and minority vulnerability were significantly associated with response calls that required emergency treatment and transport in comparison to fire-related or other calls that are also managed by fire departments. These results have implications for more strategic emergency response planning during the COVID-19 pandemic, as well as improving Total Worker Health® and future of work initiatives at the worker and workplace levels within the fire service industry.

Who continued travelling by public transport during COVID-19? Socioeconomic factors explaining travel behaviour in Stockholm 2020 based on smart card data

IntroductionThe COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden, for the spring and autumn of 2020. We suggest two binomial logit models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop travelling.Modelled variablesThe first model investigates the impact of the socioeconomic factors: age; income; education level; gender; housing type; population density; country of origin; and employment level. The results show that decreases in public transport use are linked to all these factors.The second model groups the investigated areas into five distinct clusters based on the socioeconomic data, showing the impacts for different socioeconomic groups. During the autumn the differences between the groups diminished, and especially Cluster 1 (with the lowest education levels, lowest income and highest share of immigrants) reduced their public transport use to a similar level as the more affluent clusters.ResultsThe results show that socioeconomic status affect the change in behaviour during the pandemic and that exposure to the virus is determined by citizens’ socioeconomic class. Furthermore, the results can guide policy into tailoring public transport supply to where the need is, instead of assuming that e.g. crowding is equally distributed within the public transport system in the event of a pandemic.

On the determinants and prediction of corporate financial distress in India

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

Fatores determinantes da ecoinovação no complexo eletrônico brasileiro

Ecoinovação é um conceito importante, visto como estratégia de transição para Economia Verde, Economia Circular e Ecossistemas Industriais. Internacionalmente muitos estudos vêm sendo realizados buscando seus determinantes, mas em países de baixa renda são escassos. O objetivo deste artigo é investigar os fatores determinantes das Ecoinovações nas empresas do complexo eletrônico brasileiro da Associação Brasileira da Indústria Elétrica e Eletrônica (ABINEE), por meio de pesquisa de campo e análise estatística descritiva e estimação dos modelos Logit binomial e multinomial. Foi obtida uma amostra de 12% com estimativas dos modelos significativas a 1%. Os resultados apontaram que o mercado, e não a regulação ambiental, tem sido o principal fator determinante das Ecoinovações, pois outras variáveis precisam ser incorporadas no modelo (efetiva implementação da regulação, capacitação institucional e rigor/enforcement e tipo de impacto ambiental). As Ecoinovações de produto dependem do mercado, enquanto as de processo, das regulações; firmas mais inovadoras também são Ecoinovadoras e o porte da empresa importa, mas não a origem do capital, pois muitas vezes a prática da filial não está vinculada à da matriz.

Use of fertility awareness methods as a component of safer conception for women in HIV-serodifferent relationships in Kenya

BackgroundFor couples affected by HIV, and serodifferent couples in particular, pregnancy desire is often juxtaposed against the risk of HIV transmission between the couple and the potential neonate leading to thinking about measures to minimize risk of HIV transmission. We assess the use of fertility awareness methods [FAM] and evaluate the drivers of alignment between indicators of fertility and sexual behavior among HIV-serodifferent couples desiring pregnancy.MethodsHIV-serodifferent couples from Thika, Kenya were enrolled into an open-label pilot evaluation of safer conception strategies. Women responded to daily 7-item short message service [SMS] surveys on FAM and sexual activity. Menstrual cycles were categorized as having condomless sex aligned, not aligned, or partially aligned to the predicted peak fertility. We used binomial logit models with generalized estimating equations to assess alignment between condomless sex during peak fertility days and FAM results. We used Cox proportional hazards to compare pregnancy incidence among months with sex and peak fertility aligned and mis-aligned.ResultsA total of 6929 SMS surveys across 252 menstrual cycles of 65 women were included. Reporting “sticky” cervical mucus (adjusted odds ratio [aOR]: 2.25, 95% confidence interval [95% CI]: 1.30, 3.90) and positive ovulation prediction kit [OPK] result (aOR: 2.07, 95% CI: 1.11, 3.86) were associated with increased likelihood of alignment of condomless sex during peak fertility. Pregnancy incidence was statistically similar among periods with sex aligned and not aligned with peak fertility.ConclusionsAmong women engaged in a comprehensive safer conception program, a moderate percentage of women aligned condomless sex and predicted peak fertility days at least once. While FAM, particularly cervical mucus and OPK, are an inexpensive option for couples to consider using as a component of their safer conception strategies, antiretroviral-based strategies remain important to minimize risk.

COVID-19 and Travel: How Our Out-of-home Travel Activity, In-home Activity, and Long-Distance Travel Have Changed

COVID-19 has made unprecedented impacts on our daily life. This paper investigates individuals’ immediate response to COVID-19, exploring out-of-home activities, in-home activities, and long-distance travel. Data for the Kelowna region of Canada comes from a web-based COVID−19 Survey for assessing Travel impact (COST). In addition to analyzing the survey, this research models adjustments in travel decisions by developing ordered logit models for in-home and out-of-home activities, and a binomial logit model for long-distance travel. Data analysis suggests a reduction of about 50% out-of-home activities/day/person during COVID-19 compared to the pre-pandemic period, with the only exception being picking up online orders which significantly increased in frequency. Individuals were engaged in longer duration of in-home activities; the average duration of teleworking, online shopping for groceries and other goods at-home was around 5.5 h/day/person, 32 min/day/person, and 26 min/day/person respectively. The out-of-home activity model results suggest that higher income, younger and middle aged individuals, and full-time workers are more likely to decrease their out-of-home activity; whereas, males, lower income groups, health care professionals, and picking up online orders are more likely to increase. The in-home activity model suggests that older and younger adults, higher and lower income, full-time workers, and highly educated individuals are most likely to increase their in-home activity frequency; in contrast, health care professionals are likely to decrease. Long-distance travel model results reveal that seniors, students, and airline travelers are more likely to reschedule; whereas, trips to visit friends and family are more likely to be cancelled.

Testing the binomial fixed effects logit model, with an application to female labour supply

Regression models for proportions are frequently encountered in applied work. The conditional expectation function is bounded between 0 and 1 and therefore must be nonlinear, requiring nonstandard panel data extensions. One possible approach is the binomial panel logit model with fixed effects (Machado in J Econom 119:73–98, 2004). We propose a new and simple implementation of this conditional maximum likelihood estimator for standard software. We investigate the properties of the estimator under misspecification and derive a new test for overdispersion. Estimator and test are applied in a study of contracted working volumes, measured as proportion of full-time work, for women in Switzerland.

Online and in-store purchase behavior: shopping channel choice in a developing economy

Developing economies are still in the stage of e-commerce deployment, unlike in developed countries, where online shopping is already a common purchase channel. This research aims to assess the purchasing behaviors of end-consumers in regard to two alternative shopping channels: in-store and online, within a developing economy. A revealed-preference survey was conducted to collect the in-store and online purchase activity of households in Ukraine. The data collected presents the purchase channel choices of end-consumers for eleven commodities in two categories: experience goods and search goods. A descriptive analysis of in-store and online shopping was made, with an evaluation of average purchase cost and time expenditures for shopping, travel, and delivery processes. A pooled binomial logit model was then developed to assess the purchase channel choice based on a Random Utility Maximization Theory. The estimated values of a marginal probability effect are presented, and the significance levels of attributes influencing purchase channel choice are evaluated. The marginal probability effect is found to be greater for shopping cost than for time attributes for most of the studied commodity channel choices. The sensitivity assessment for purchase cost, delivery and travel time revealed that first-priority goods such as medicine, food, clothing and shoes depended more on the attributes’ values variation than other commodities considered in the study. The comparison of this research’s results with other studies has shown a higher importance of shopping cost than time attributes for channel choice decisions in the case of the developing economy.

Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

ABSTRACT Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport.

MODELING SPATIAL EFFECT ON TRAVEL MODE CHOICE USING A SYNTHETIC SPATIALLY CORRELATED DATA SET

Abstract: Urban dynamics can be characterized more effectively by considering spatial aspects in studies. This paper, using a synthetic spatially correlated data set, aims to model the spatial effect on travel mode choice based on geostatistics precepts. A method was proposed based on three main steps. The first step consists of building synthetic spatially correlated data, using the intrinsic spatial dependence on travel demand data and mathematical principles of bilinear interpolation. The following two steps correspond to the modeling approach. The Exploratory Spatial Data Analysis stage aimed to attest the existence of spatial autocorrelation of the data set using two indicators: Moran and G-SIVAR (Global Spatial Indicator Based on Variogram). The Confirmatory Spatial Data Analysis stage proposed the calibration of two Binomial Logit models. The first model includes only the original database variables (non-spatial model). The second one is analogous to the original but added to spatial covariates obtained by geostatistical concepts (spatial model). A 15% increase in cross-validation hit rates is achieved when spatial variables are included. This paper presents three significant research contributions: (1) The methodological procedure to model spatial effect on travel mode choice; (2) The proposal of spatial covariates based on geostatistical assumptions; and (3) The suggestion of a simple procedure to propose a simulation of a spatially correlated database.

System of Guarantees and Access to Leasing: the Case of Entreprises in the Republic of Congo

The use of leasing and/or bank lending has always generated a very rich and controversial debate in the economic literature. It is generally accepted that leasing is a complement and not a substitute for bank lending in corporate finance. In this thesis, we will study the impact of leasing and/or bank credit on the activity of Congolese companies. To do so, we use the binomial logit model to identify the factors that explain the use of bank credit and/or leasing. Findings from our results show that companies use bank credit more than leasing.