Probit Model Research Articles

A Study of the Impact of Household Wealth Accumulation on the Well-being of the Population

Happiness is the ultimate goal of a good life for residents, and high-quality wealth accumulation is an important force for improving people's well-being. Based on the 2015, 2017 and 2019 China Household Finance Survey (CHFS) survey data, this paper examines the effect of household wealth on residents' happiness using a fixed-effects probit model. It is found that, first, an increase in household wealth significantly increases residents' happiness. The greater the accumulation of household wealth, the happier the residents, and the conclusion still holds after a series of robustness tests. Second, household wealth accumulation has a more pronounced effect on the happiness of residents in the eastern region and a stronger effect on the happiness of urban residents. Third, family wealth accumulation has a stronger effect on the happiness of residents with middle and high levels of education than those with low levels of education. The conclusions of the study are of great significance for regulating the mechanism of family wealth accumulation, strengthening residents' sense of well-being and promoting the high-quality development of society.

Adoption of Coffee Technologies: A Multivariate Probit Model

The sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, and sufficient rainfall in coffee growing belts of the country. Adoption of improved coffee varieties and recommended coffee management practice together have a significant effect on annual coffee production. The study was aimed to see the adoption rate and intensity of coffee technologies which are improved coffee Varity and coffee management practice, determinant of adoption of coffee technologies in Jima zone south western Ethiopia. A total of 196 sampled households from three woreda in the zone and 430 plots of 196 farmers household is considered in the survey. The study develops a multivariate probit model econometric model of farmers' choice of combination of coffee technologies. And two primary results were found. First, adoption rate and intensity of Improved coffee variety is greater than adoption of coffee management practice. Secondly adoption of coffee technologies determined by many institutional, resource and other related factor. This implies that policy makers and other stakeholders promoting a combination of technologies can enhance coffee yield through reducing production costs and decreasing coffee vulnerability to disease.

The Effects of Monitoring Activities on Loan Defaults in Group-Based Lending Program: Evidence from Vietnam

The aim of this study is to investigate the impact of delegated monitoring by a group leader and peer monitoring by group members on loan defaults in a group-based lending program in Vietnam. The data used in the study were collected from a questionnaire survey of 675 participants involved in a group-based lending program conducted from August to October 2022 in the Mekong River Delta, Vietnam. This group-based lending program employs a unique monitoring system that involves hiring the group leader to supervise the group and encouraging group members to monitor each other. The empirical findings derived from the Probit model indicated that delegated monitoring significantly reduces loan defaults, but there was no evidence supporting the effectiveness of peer monitoring within the group. Additionally, under the delegated monitoring scheme, commissions and group size plays an important role in decreasing loan defaults. The implication of the findings is that the Vietnam Bank for Social Policies (VBSP) could maintain large group sizes to provide incentives for group leaders through commissions to enhance repayment rates.

Logit and Probit Models Explaining Mode Choice and Frequency of Public Transit Ridership among University Students in Krakow, Poland

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries.

Benzalkonium chloride induced acute toxicity and its multifaceted implications on growth, hematological metrics, biochemical profiles, and stress-responsive biomarkers in tilapia (Oreochromis mossambicus).

This study aimed to investigate the toxic effects of benzalkonium chloride (BAC) on Oreochromis mossambicus, a freshwater fish species. Probit analysis was used to determine the lethal concentration (LC50) of BAC for different exposure periods (24, 48, 72, and 96h). The viability of fish exposed to BAC was assessed using the general threshold survival models (GUTS) and confirmed with relevant datasets to evaluate model accuracy. Experimental groups of fish were exposed to BAC concentrations equivalent to 10% and 20% of the 96-h LC50 for 45days. The study revealed significant alterations in various parameters during sublethal BAC exposure. These effects included decreased specific growth rate (SGR), red blood cell count (RBC), hemoglobin (Hb) concentration, hematocrit (Ht) value, plasma protein, and albumin levels, as well as acetylcholinesterase (AChE) activities in both gills and liver. Additionally, an increase in gastrosomatic index (GSI), feed conversion ratio (FCR), plasma glucose and creatinine concentrations, alanine aminotransferase (ALT), aspartate aminotransferase (AST) enzymatic activities, catalase (CAT), superoxide dismutase (SOD), and malondialdehyde (MDA) levels were observed in the exposed fish's gills and liver. Furthermore, the study found that glutathione S-transferase (GST) and glutathione peroxidase (GPx) levels initially increased and then decreased in both gills and liver after exposure to BAC. Correlation matrix analysis, multivariate multiple regression (MMR), canonical correspondence analysis (CCA), integrated biomarker response (IBR), and biomarker response index (BRI) were utilized to assess the impact of BAC on fish, highlighting significant effects on multiple biomarkers in O. mossambicus following surfactant exposure. Thus, the study provides valuable insights into the toxic effects of BAC on this fish species, emphasizing the importance of monitoring such pollutants in aquatic environments.

Automated recommendation model using ordinal probit regression factorization machines

Automated recommendation model using ordinal probit regression factorization machines

Multilevel social insurance needs and household financial asset allocation

This study selects data from the China Household Finance survey 2019, constructs the residents’ financial literacy index using factor analysis, and uses the probit model to examine the impact of multilevel social insurance demand on household financial asset allocation. This study finds that multilevel insurance demand weakens household financial asset allocation while other factors remain constant; the age of the household head negatively affects the allocation of household risky financial assets; household financial literacy moderates multilevel insurance demand and household financial allocation.

Transitioning towards sustainable agriculture: analysing the factors and impact of adopting multiple sustainable inputs by paddy farmers in India

IntroductionThere is a global priority to ensure food security, environmental conservation and sustainability. The adoption of inputs such as organic manures, biofertilisers, and biopesticides has been widely promoted as a way to enhance agricultural sustainability, yet their adoption rates remain notably low. The study examines the determinants and impact of the adoption of multiple sustainable inputs by paddy farmers in India. The study also examines the drivers behind the intensity of their adoption and their technical efficiency.MethodsThe study utilises the survey dataset on paddy farmers from “Situation Assessment of Agricultural Households and Land and Livestock Holdings of Households in Rural India” of the 77th National Sample Survey (NSSO) round. The reference period for data collection was the agricultural year from July 2018 to June 2019. The analytical methods employed include the Multivariate Probit and Ordered Probit models, Seemingly Unrelated Regression, Regression Adjustment model, and Stochastic Frontier method.ResultsThe empirical findings reveal that the adoption and intensity of sustainable inputs depend significantly on factors such as the household head’s age and education, household size, and access to extension services. Adopting sustainable inputs has significant benefits, such as increased harvest value. However, the currently available technical advisory services are limited in effectively promoting the expenditure on sustainable inputs. Technical efficiency analysis underscores the potential for improvement and the importance of utilising optimal input levels. The research findings provide strong evidence in favour of advocating for the use of sustainable inputs in paddy farming.

Determinant of importance-performance and willingness to participate in Komodo adaptive conservation programs

This study delves into the evaluation of the Komodo dragons Adaptive Conservation Program (KACP) at Komodo National Park in Indonesia through Importance-Performance Analysis (IPA). It aims to understand how the programs components align with community priorities and assess its effectiveness. Utilizing a mixed-methods approach, this research analyzes community perceptions of KACP’s critical elements, spanning eco-tourism initiatives to environmental protection strategies. The community is categorized into local advocates, eco-tourism enthusiasts, and eco-community harmonizers, identifying how each group perceives and interacts with various facets of KACP. The findings reveal significant differences in perceptions of importance and performance among these groups, providing insights into areas of strength and those needing improvement. Additionally, the study includes an analysis of demographic influences on these perceptions through Logit and Probit models, aiming to understand the impacts across different community segments. The results highlight the necessity for an adaptive and responsive design in conservation efforts, considering community expectations and ecological impacts. Despite offering comprehensive insights, the study acknowledges limitations in the demographic scope and suggests future research directions, such as longitudinal studies and an expanded analysis of variables. These findings offer critical guidance for policymakers and conservation practitioners to enhance conservation programs’ effectiveness and community alignment, emphasizing the need to adapt to the challenges and changes introduced by KACP.

Cognitive functioning and sustained internet use amid the COVID-19 pandemic: longitudinal evidence from older adults in Switzerland

This study aimed to investigate the relationship between pre-pandemic objective and subjective cognitive functioning and sustained Internet use during the pandemic among older adults in Switzerland. Data from 1299 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2019/2020 and a supplementary technology use questionnaire during the pandemic in 2021 were used. Cognitive functioning was assessed in 2019/2020 through objective measures (delayed and immediate memory, verbal fluency) and self-rated memory. Sustained Internet use was defined as having used the Internet at least once in the past seven days in 2019/2020 and reporting daily or weekly use in 2021. We found that 73.1% of respondents consistently used Internet between 2019/2020 and 2021. Using multivariable probit regression models controlling for sociodemographic and health variables, we found that higher global cognition z-scores, especially in immediate and delayed memory, were associated with a higher likelihood of sustained Internet use. Additionally, respondents with good, very good, or excellent self-rated memory were more likely to sustain their Internet use. These findings highlight the potential critical role of cognitive health in shaping older adults’ digital engagement, suggesting that cognitive assessments and training should be further considered in digital literacy initiatives for this population.

Bayesian analysis of multivariate mixed longitudinal ordinal and continuous data

SummaryMultivariate longitudinal ordinal and continuous data exist in many scientific fields. However, it is a rigorous task to jointly analyse them due to the complicated correlated structures of those mixed data and the lack of a multivariate distribution. The multivariate probit model, assuming there is a multivariate normal latent variable for each multivariate ordinal data, becomes a natural modeling choice for longitudinal ordinal data especially for jointly analysing with longitudinal continuous data. However, the identifiable multivariate probit model requires the variances of the latent normal variables to be fixed at 1, thus the joint covariance matrix of the latent variables and the continuous multivariate normal variables is restricted at some of the diagonal elements. This constrains to develop both the classical and Bayesian methods to analyse mixed ordinal and continuous data. In this investigation, we proposed three Markov chain Monte Carlo (MCMC) methods: Metropolis–Hastings within Gibbs algorithm based on the identifiable model, and a Gibbs sampling algorithm and parameter‐expanded data augmentation based on the constructed non‐identifiable model. Through simulation studies and a real data application, we illustrated the performance of these three methods and provided an observation of using non‐identifiable model to develop MCMC sampling methods.

Simple closed-form estimation of a binary latent variable model

Abstract This paper develops a closed-form nonparametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: i) it has a closed-form expression for all the sieve coefficients; ii) it is computationally straightforward, equivalent to computing eigenvalues and eigenvectors of matrices without the use of iterative optimization algorithms. While as flexible as the sieve maximum likelihood estimator (MLE) previously proposed for this model, our estimator proves computationally simpler. The finite sample properties of the estimator are investigated through a Monte Carlo study, and the developed estimator is applied to a probit model to assess the targeting performance of a social welfare program.

INTERGENERATIONAL TRANSMISSION OF MATERNAL EMPLOYMENT AND DAUGHTERS' CAREER PATHS: EVIDENCE FROM THE 2021 FAMILY STRUCTURE SURVEY

The study investigates the intergenerational transmission of maternal employment to daughters’ employment outcomes using data from the 2021 Family Structure Survey. The results of estimations based on a linear probability model (LPM) and a probit model indicate a positive link between maternal employment and daughters’ employment. Furthermore, multinomial logistic regression results demonstrate that mothers working as salaried workers increases the probability of daughters to work as salaried workers. Similarly, mothers who have been employed as unpaid family workers transmit this employment status to their daughters. This pattern is consistent for mothers who have held positions as day laborers, employers, or have been self-employed, as they also influence their daughters’ employment in similar roles. The estimation findings also highlight the significant impact of higher education on the likelihood of daughters becoming salaried workers, indicating that higher levels of education substantially contribute to this outcome. The study provides valuable evidence into the intergenerational influence of maternal employment on daughters’ career paths.

Catastrophic health expenditure of inpatients in emerging economies: evidence from the Indian subcontinent

BackgroundCatastrophic health expenditures condensed the vital concern of households struggling with notable financial burdens emanating from elevated out-of-pocket healthcare expenditures. In this regard, this study investigated the nature and magnitude of inpatient healthcare expenditure in India. It also explored the incidence and determinants of inpatient catastrophic health expenditure.MethodologyThe study used the micro-level data collected in the 75th Round of the National Sample Survey on 93 925 households in India. Descriptive statistics were used to examine the nature, magnitude and incidence of inpatient healthcare expenditure. The heteroscedastic probit model was applied to explore the determinants of inpatient catastrophic healthcare expenditure.ResultsThe major part of inpatient healthcare expenditure was composed of bed charges and expenditure on medicines. Moreover, results suggested that Indian households spent 11% of their monthly consumption expenditure on inpatient healthcare and 28% of households were grappling with the complexity of financial burden due to elevated inpatient healthcare. Further, the study explored that bigger households and households having no latrine facilities and no proper waste disposal plans were more vulnerable to facing financial burdens in inpatient healthcare activity. Finally, the result of this study also ensure that households having toilets and safe drinking water facilities reduce the chance of facing catastrophic inpatient health expenditures.ConclusionsA significant portion of monthly consumption expenditure was spent on inpatient healthcare of households in India. It was also conveyed that inpatient healthcare expenditure was a severe burden for almost one fourth of households in India. Finally, it also clarified the influence of socio-economic conditions and sanitation status of households as having a strong bearing on their inpatient healthcare.

Factors Influencing the Perceived Economic Benefits of Innovative Agri-Environmental Contracts

Continued innovation in contract design may enhance the delivery of agri-environmental climate public goods (AECPG), but barriers to adoption arise in terms of how farmers perceive the economic benefits. Therefore, this paper examines survey data from Ireland and the Netherlands to analyse whether land managers agree that results-based, collective action, value chain and land tenure contracts for the delivery of AECPG are understandable, applicable to their farm and economically beneficial. Using Probit models, we also identify groups of land managers who perceive the different contract types as being economically beneficial, and these findings can inform policymakers of farmer groups who need adequate consideration during the design of agri-environmental contracts. For example, greater incentives could encourage older farmers to enrol in results-based contracts in Ireland and value chain contracts in the Netherlands. We also find a link between contract duration and the perceived economic benefits of collective action contracts in both countries, with land managers in Ireland desiring a longer duration. We highlight that policymakers and land managers in Ireland could apply lessons from the design of agri-environmental contracts in the Netherlands, where they are more common and varied. Greater knowledge exchange between users and non-users of such contracts would also help bridge the gap between theory and practice in both countries.

Digital Financial Capability and Entrepreneurship in China: A Digital Economy Perspective

The digital economy is the fastest-growing sector in the world today, and will also be the key development direction of China in the future. The level of digital financial capability (DFC) determines whether households can fully embrace the inclusivity of digital finance and seize the business opportunities presented by the digital economy. Hence, we utilized panel data from the China Household Finance Survey (CHFS) and built a probit model with two-way fixed effects. The results revealed that DFC has a positive impact on household entrepreneurship through both supply- and demand-side mechanisms. The results also revealed that DFC has not only catalyzed the evolution of digital business models but has also enhanced the digital business performance of enterprises. The analysis of heterogeneity confirms that DFC can promote entrepreneurship in rural settings and in the retail and restaurant sectors. This research has important theoretical contributions and policy suggestions for governments to help them pay attention to the inclusive role of DFC in developing the digital economy.

Language Barriers Impact on Trade Disputes: Evidence from the WTO Trade Disputes Cases

This study examines the impact of language barriers on trade disputes using a panel data set of 129 countries and 565 trade dispute cases from the World Trade Organization (WTO) spanning the years 1995 to 2018. The language barrier index is employed as a metric to assess the extent of language barriers between trade nations, while the panel probit model is utilized for conducting the empirical analysis. The empirical evidence indicates that language barriers exert a substantial and favorable impact on trade disputes. Language obstacles in trading countries have been found to heighten the probability of trade disputes, indicating that language barriers contribute to increase trade costs and hamper bilateral trade relations. Moreover, the Armington model is employed to elucidate the theoretical aspects of the impact of language barriers on trade disputes, with trade costs serving as the mediating factor. The findings of the study also validated the notion that linguistic barriers contribute to an elevated likelihood of trade disputes. This research study presents empirical findings regarding the influence of linguistic barriers on trade disputes. Additional investigation can be undertaken to examine the impact of language competency on trade disputes, given the substantial role that language plays in the realm of international trade.

Analysis of residents’ health and willingness to pay for environmental protection in China

Health and environmental issues have become pressing global problems that affect human society's sustainable development. Residents are not only practitioners but also direct beneficiaries of ecological protection and play a crucial role in maintaining ecological and environmental health. Health, as the basis of residents' behavioral choices, influences residents' behavior toward ecological and environmental protection. This study aims to investigate how health factors profoundly affect residents' willingness to pay (WTP) for environmental protection and reveal the intrinsic connection between health and environmental protection behavior. The 2017 China Household Finance Survey (CHFS) data and the ordered probit model were used to determine that improving residents' health greatly increases their willingness to protect the environment and passes a number of tough tests. In addition, the promotion effect of health on residents' WTP for ecological protection is markedly heterogeneous, especially in younger residents who do not have children. Therefore, we suggest to policymakers and national governments a range of countermeasures and suggestions to enhance health education, safeguard public health, and promote ecological protection education.

Assessment of transit bus electricity consumption using a random parameters approach

The shift towards sustainable transportation emphasizes the crucial role of electric transit buses in promoting cleaner and more efficient public transit systems. This study conducts an explanatory analysis of electric bus electricity consumption, with a focus on the impacts of detailed driver behavior and vehicle operation. A random parameters ordered probit model is applied to an electric bus dataset collected from Guangzhou, China. Findings indicate that some factors exhibit consistent impacts on electricity consumption across bus fleets, such as the acceleration rate at bus stops and the number of stops induced by traffic conditions. However, variability exists within the bus fleet regarding the effects of other factors, such as the average speed and handbrake usage. These results underscore the need for targeted strategies, such as maintenance, route optimization, driver training, and policy adjustments in bus operations, to decrease electricity consumption and improve the efficiency of public transit systems.

Effects of Radio-Based Extension Services on farmers’ Adoption of Organo-Mineral Fertilizers, Biofertilizers, and Manure in Lesotho

ABSTRACT This study examined the effects of radio-based extension services on farmers’ adoption of organo-mineral fertilizer, biofertilizer, and manure in Lesotho using a sample of 1659 farmers extracted from the 2019/2020 agricultural production survey database Bureau of Statistics (BOS). Data were analyzed using Statistical Package for the Social Sciences (SPSS) Version 29 to analyze and compare the data collected. Descriptive statistics were computed to examine the proportion and disaggregation based on the use of radio as the main source of extension information through cross-tabulation and a Probit regression analysis. The results show information types covered include agronomy, pest/disease, credit, new practice, varieties, weather, land tenure, soil conservation, and crop protection, with majority indicating radio as the source; while technologies promoted by radio are soil conservation, terraces, cover cropping, crop rotation, organo-mineral fertilizer, organic fertilizer, biofertilizers, manure, and improved seeds. Radio continues to play a major role in the dissemination of agricultural information and influence adoption behavior despite the multimedia approach to agricultural communication.