Commenced in January 2007
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Edition: International
Paper Count: 12677

Search results for: probit model

12677 Determinants of Self-Reported Hunger: An Ordered Probit Model with Sample Selection Approach

Authors: Brian W. Mandikiana


Homestead food production has the potential to alleviate hunger, improve health and nutrition for children and adults. This article examines the relationship between self-reported hunger and homestead food production using the ordered probit model. A sample of households participating in homestead food production was drawn from the first wave of the South African National Income Dynamics Survey, a nationally representative cross-section. The sample selection problem was corrected using an ordered probit model with sample selection approach. The findings show that homestead food production exerts a positive and significant impact on children and adults’ ability to cope with hunger and malnutrition. Yet, on the contrary, potential gains of homestead food production are threatened by shocks such as crop failure.

Keywords: agriculture, hunger, nutrition, sample selection

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12676 Identifying Key Factors for Accidents’ Severity at Rail-Road Level Crossings Using Ordered Probit Models

Authors: Arefeh Lotfi, Mahdi Babaei, Ayda Mashhadizadeh, Samira Nikpour, Morteza Bagheri


The main objective of this study is to investigate the key factors in accidents’ severity at rail-road level crossings. The data required for this study is obtained from both accident and inventory database of Iran Railways during 2009-2015. The Ordered Probit model is developed using SPSS software to identify the significant factors in the accident severity at rail-road level crossings. The results show that 'train speed', 'vehicle type' and 'weather' are the most important factors affecting the severity of the accident. The results of these studies assist to allocate resources in the right place. This paper suggests mandating the regulations to reduce train speed at rail-road level crossings in bad weather conditions to improve the safety of rail-road level crossings.

Keywords: rail-road level crossing, ordered probit model, accidents’ severity, significant factors

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12675 Determinants of Rural Household Effective Demand for Biogas Technology in Southern Ethiopia

Authors: Mesfin Nigussie


The objectives of the study were to identify factors affecting rural households’ willingness to install biogas plant and amount willingness to pay in order to examine determinants of effective demand for biogas technology. A multistage sampling technique was employed to select 120 respondents for the study. The binary probit regression model was employed to identify factors affecting rural households’ decision to install biogas technology. The probit model result revealed that household size, total household income, access to extension services related to biogas, access to credit service, proximity to water sources, perception of households about the quality of biogas, perception index about attributes of biogas, perception of households about installation cost of biogas and availability of energy source were statistically significant in determining household’s decision to install biogas. Tobit model was employed to examine determinants of rural household’s amount of willingness to pay. Based on the model result, age of the household head, total annual income of the household, access to extension service and availability of other energy source were significant variables that influence willingness to pay. Providing due considerations for extension services, availability of credit or subsidy, improving the quality of biogas technology design and minimizing cost of installation by using locally available materials are the main suggestions of this research that help to create effective demand for biogas technology.

Keywords: biogas technology, effective demand, probit model, tobit model, willingnes to pay

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12674 Household Level Determinants of Rural-Urban Migration in Bangladesh

Authors: Shamima Akhter, Siegfried Bauer


The aim of this study is to analyze the migration process of the rural population of Bangladesh. Heckman Probit model with sample selection was applied in this paper to explore the determinants of migration and intensity of migration at farm household level. The farm survey was conducted in the central part of Bangladesh on 160 farm households with migrant and on 154 farm households without migrant including a total of 316 farm households. The results from the applied model revealed that main determinants of migration at farm household level are household age, economically active males and females, number of young and old dependent members in the household and agricultural land holding. On the other hand, the main determinants of intensity of migration are availability of economically adult male in the household, number of young dependents and agricultural land holding.

Keywords: determinants, Heckman Probit model, migration, rural-urban

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12673 Assessing Effects of an Intervention on Bottle-Weaning and Reducing Daily Milk Intake from Bottles in Toddlers Using Two-Part Random Effects Models

Authors: Yungtai Lo


Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-log-normal random effects model, a two-part logit-truncated normal random effects model, a two-part logit-gamma random effects model, and a two-part logit-skew normal random effects model were used to examine effects of a bottle-weaning intervention on reducing bottle use and daily milk intake from bottles in toddlers aged 11 to 13 months in a randomized controlled trial. We show in all four two-part models that the intervention promoted bottle-weaning and reduced daily milk intake from bottles in toddlers drinking from a bottle. We also show that there are no differences in model fit using either the logit link function or the probit link function for modeling the probability of bottle-weaning in all four models. Furthermore, prediction accuracy of the logit or probit link function is not sensitive to the distribution assumption on daily milk intake from bottles in toddlers not off bottles.

Keywords: two-part model, semi-continuous variable, truncated normal, gamma regression, skew normal, Pearson residual, receiver operating characteristic curve

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12672 An Empirical Analysis of Euthanasia Issues in Taiwan

Authors: Wen-Shai Hung


This paper examines the factors influencing euthanasia issues in Taiwan. The data used is from the 2015 Survey Research on Attitudes towards the Death Penalty and Related Values in Taiwan, which focused on knowledge, attitudes towards the death penalty, and the concepts of social, political, and law values. The sample ages are from 21 to 94. The method used is probit modelling for examining the influences on euthanasia issues in Taiwan. The main empirical results find that older people, persons with higher educational attainment, those who favour abolition of the death penalty and do not oppose divorce, abortion, same-sex relationships, and putting down homeless’ cats or dogs are more likely to approve of the use of euthanasia to end their lives. In contrast, Mainlanders, people who support the death penalty and favour long-term prison sentences are less likely to support the use of euthanasia.

Keywords: euthanasia, homosexual, death penalty, and probit model

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12671 An Assessment into the Drift in Direction of International Migration of Labor: Changing Aspirations for Religiosity and Cultural Assimilation

Authors: Syed Toqueer Akhter, Rabia Zulfiqar


This paper attempts to trace the determining factor- as far as individual preferences and expectations are concerned- of what causes the direction of international migration to drift in certain ways owing to factors such as Religiosity and Cultural Assimilation. The narrative on migration has graduated from the age long ‘push/pull’ debate to that of complex factors that may vary across each individual. We explore the longstanding factor of religiosity widely acknowledged in mentioned literature as a key variable in the assessment of migration, wherein the impact of religiosity in the form of a drift into the intent of migration has been analyzed. A more conventional factor cultural assimilation is used in a contemporary way to estimate how it plays a role in affecting the drift in direction. In particular what our research aims at achieving is to isolate the effect our key variables: Cultural Assimilation and Religiosity have on direction of migration, and to explore how they interplay as a composite unit- and how we may be able to justify the change in behavior displayed by these key variables. In order to establish a true sense of what drives individual choices we employ the method of survey research and use a questionnaire to conduct primary research. The questionnaire was divided into six sections covering subjects including household characteristics, perceptions and inclinations of the respondents relevant to our study. Religiosity was quantified using a proxy of Migration Network that utilized secondary data to estimate religious hubs in recipient countries. To estimate the relationship between Intent of Migration and its variants three competing econometric models namely: the Ordered Probit Model, the Ordered Logit Model and the Tobit Model were employed. For every model that included our key variables, a highly significant relationship with the intent of migration was estimated.

Keywords: international migration, drift in direction, cultural assimilation, religiosity, ordered probit model

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12670 Educational Deprivation and Their Determinants in India: Evidence from National Sample Survey

Authors: Mukesh Ranjan


Applying probit model on the micro data of NSS 71st round on education for understanding the access to education post the passage of Right to Education act,2009 in India. The empirical analysis shows that at all India level the mean age of enrollment in school is 5.5 years and drop-out age is around 14 years (or studied up to class 7) and around 60 percent females never get enrolled in any school in their lifetime. Nearly 20 percent children in Bihar never seen school and surprisingly, the relatively developed states like Gujarat, Maharashtra, Karnataka, Kerala and Tamil Nadu have more than one-third of the children and half of the children in Andhra Pradesh, West Bengal and Orissa as educationally wasted. The relative contribution in educational wastage is maximum by Bengal (10 %) while UP contributed a maximum of 30 % in educational non-enrollment in the country. Educational wastage is more likely to increase with age. Marriage is a resistive factor in getting education. Muslims are educationally more deprived than Hindus. Larger family and rich household are less likely to be educationally deprived. Major reasons for drop-out until 9 years were lack of interest in education and financial constraint; between 10-12 years, lack of interest and unable to cope up with studies and post 12 years financial constraint, marriage and other household reasons.

Keywords: probit model, educational wastage, educational non-enrollment, educational deprivation

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12669 Machine Learning Approach for Stress Detection Using Wireless Physical Activity Tracker

Authors: B. Padmaja, V. V. Rama Prasad, K. V. N. Sunitha, E. Krishna Rao Patro


Stress is a psychological condition that reduces the quality of sleep and affects every facet of life. Constant exposure to stress is detrimental not only for mind but also body. Nevertheless, to cope with stress, one should first identify it. This paper provides an effective method for the cognitive stress level detection by using data provided from a physical activity tracker device Fitbit. This device gathers people’s daily activities of food, weight, sleep, heart rate, and physical activities. In this paper, four major stressors like physical activities, sleep patterns, working hours and change in heart rate are used to assess the stress levels of individuals. The main motive of this system is to use machine learning approach in stress detection with the help of Smartphone sensor technology. Individually, the effect of each stressor is evaluated using logistic regression and then combined model is built and assessed using variants of ordinal logistic regression models like logit, probit and complementary log-log. Then the quality of each model is evaluated using Akaike Information Criterion (AIC) and probit is assessed as the more suitable model for our dataset. This system is experimented and evaluated in a real time environment by taking data from adults working in IT and other sectors in India. The novelty of this work lies in the fact that stress detection system should be less invasive as possible for the users.

Keywords: physical activity tracker, sleep pattern, working hours, heart rate, smartphone sensor

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12668 Households’ Willingness to Pay for Watershed Management Practices in Lake Hawassa Watershed, Southern Ethiopia

Authors: Mulugeta Fola, Mengistu Ketema, Kumilachew Alamerie


Watershed provides vast economic benefits within and beyond the management area of interest. But most watersheds in Ethiopia are increasingly facing the threats of degradation due to both natural and man-made causes. To reverse these problems, communities’ participation in sustainable management programs is among the necessary measures. Hence, this study assessed the households’ willingness to pay for watershed management practices through a contingent valuation study approach. Double bounded dichotomous choice with open-ended follow-up format was used to elicit the households’ willingness to pay. Based on data collected from 275 randomly selected households, descriptive statistics results indicated that most households (79.64%) were willing to pay for watershed management practices. A bivariate Probit model was employed to identify determinants of households’ willingness to pay and estimate mean willingness to pay. Its result shows that age, gender, income, livestock size, perception of watershed degradation, social position, and offered bids were important variables affecting willingness to pay for watershed management practices. The study also revealed that the mean willingness to pay for watershed management practices was calculated to be 58.41 Birr and 47.27 Birr per year from the double bounded and open-ended format, respectively. The study revealed that the aggregate welfare gains from watershed management practices were calculated to be 931581.09 Birr and 753909.23 Birr per year from double bounded dichotomous choice and open-ended format, respectively. Therefore, the policymakers should make households to pay for the services of watershed management practices in the study area.

Keywords: bivariate probit model, contingent valuation, watershed management practices, willingness to pay

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12667 Working Children and Adolescents and the Vicious Circle of Poverty from the Perspective of Gunnar Myrdal’s Theory of Circular Cumulative Causation: Analysis and Implementation of a Probit Model to Brazil

Authors: J. Leige Lopes, L. Aparecida Bastos, R. Monteiro da Silva


The objective of this paper is to study the work of children and adolescents and the vicious circle of poverty from the perspective of Guinar Myrdal’s Theory of Circular Cumulative Causation. The objective is to show that if a person starts working in the juvenile phase of life they will be classified as poor or extremely poor when they are adult, which can to be observed in the case of Brazil, more specifically in the north and northeast. To do this, the methodology used was statistical and econometric analysis by applying a probit model. The main results show that: if people reside in the northeastern region of Brazil, and if they have a low educational level and if they start their professional life before the age 18, they will increase the likelihood that they will be poor or extremely poor. There is a consensus in the literature that one of the causes of the intergenerational transmission of poverty is related to child labor, this because when one starts their professional life while still in the toddler or adolescence stages of life, they end up sacrificing their studies. Because of their low level of education, children or adolescents are forced to perform low-paid functions and abandon school, becoming in the future, people who will be classified as poor or extremely poor. As a result of poverty, parents may be forced to send their children out to work when they are young, so that in the future they will also become poor adults, a process that is characterized as the "vicious circle of poverty."

Keywords: children, adolescents, Gunnar Myrdal, poverty, vicious circle

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12666 Predicting Recessions with Bivariate Dynamic Probit Model: The Czech and German Case

Authors: Lukas Reznak, Maria Reznakova


Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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12665 Use of SUDOKU Design to Assess the Implications of the Block Size and Testing Order on Efficiency and Precision of Dulce De Leche Preference Estimation

Authors: Jéssica Ferreira Rodrigues, Júlio Silvio De Sousa Bueno Filho, Vanessa Rios De Souza, Ana Carla Marques Pinheiro


This study aimed to evaluate the implications of the block size and testing order on efficiency and precision of preference estimation for Dulce de leche samples. Efficiency was defined as the inverse of the average variance of pairwise comparisons among treatments. Precision was defined as the inverse of the variance of treatment means (or effects) estimates. The experiment was originally designed to test 16 treatments as a series of 8 Sudoku 16x16 designs being 4 randomized independently and 4 others in the reverse order, to yield balance in testing order. Linear mixed models were assigned to the whole experiment with 112 testers and all their grades, as well as their partially balanced subgroups, namely: a) experiment with the four initial EU; b) experiment with EU 5 to 8; c) experiment with EU 9 to 12; and b) experiment with EU 13 to 16. To record responses we used a nine-point hedonic scale, it was assumed a mixed linear model analysis with random tester and treatments effects and with fixed test order effect. Analysis of a cumulative random effects probit link model was very similar, with essentially no different conclusions and for simplicity, we present the results using Gaussian assumption. R-CRAN library lme4 and its function lmer (Fit Linear Mixed-Effects Models) was used for the mixed models and libraries Bayesthresh (default Gaussian threshold function) and ordinal with the function clmm (Cumulative Link Mixed Model) was used to check Bayesian analysis of threshold models and cumulative link probit models. It was noted that the number of samples tested in the same session can influence the acceptance level, underestimating the acceptance. However, proving a large number of samples can help to improve the samples discrimination.

Keywords: acceptance, block size, mixed linear model, testing order, testing order

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12664 Modeling Default Probabilities of the Chosen Czech Banks in the Time of the Financial Crisis

Authors: Petr Gurný


One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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12663 Modelling the Effect of Psychological Capital on Climate Change Adaptation among Smallholders from South Africa

Authors: Unity Chipfupa, Aluwani Tagwi, Edilegnaw Wale


Climate change adaptation studies are challenged by a limited understanding of how non-cognitive factors such as psychological capital affect adaptation decisions of smallholder farmers. The concept of psychological capital has not been fully applied in the empirical literature on climate change adaptation strategies. Hence, the study was meant to assess how psychological capital endowment affects climate change adaptation among smallholder farmers. A multivariate probit regression model was estimated using data collected from 328 smallholder farmers in KwaZulu-Natal, South Africa. The findings indicate that, among other factors, self-confidence and hope or aspirations in farming influence climate change adaptation decisions of smallholders. The psychological capital theory proved to be comprehensive in identifying specific psychological dimensions associated with adaptation decisions. However, the non-alignment of approaches for measuring non-cognitive factors made it difficult to compare results among different studies. In conclusion, the study recommends the need for practical ways for enhancing smallholders’ endowment with key non-cognitive abilities. Researchers should develop and agree on a comprehensive framework for assessing non-cognitive factors critical for climate change adaptation. This will improve the use of positive psychology theories to advance the literature on climate change adaptation. Other key recommendations include targeted support for communities facing higher risks of climate change, improving smallholders’ ability to adapt, promotion of social networks and the inclusion of farming objectives as an important indicator in climate change adaptation research.

Keywords: adaptive capacity, climate change adaptation, psychological capital, multivariate probit, non-cognitive factors.

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12662 Behavior Loss Aversion Experimental Laboratory of Financial Investments

Authors: Jihene Jebeniani


We proposed an approach combining both the techniques of experimental economy and the flexibility of discrete choice models in order to test the loss aversion. Our main objective was to test the loss aversion of the Cumulative Prospect Theory (CPT). We developed an experimental laboratory in the context of the financial investments that aimed to analyze the attitude towards the risk of the investors. The study uses the lotteries and is basing on econometric modeling. The estimated model was the ordered probit.

Keywords: risk aversion, behavioral finance, experimental economic, lotteries, cumulative prospect theory

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12661 A-Score, Distress Prediction Model with Earning Response during the Financial Crisis: Evidence from Emerging Market

Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro


Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

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12660 A New Nonlinear State-Space Model and Its Application

Authors: Abdullah Eqal Al Mazrooei


In this work, a new nonlinear model will be introduced. The model is in the state-space form. The nonlinearity of this model is in the state equation where the state vector is multiplied by its self. This technique makes our model generalizes many famous models as Lotka-Volterra model and Lorenz model which have many applications in the real life. We will apply our new model to estimate the wind speed by using a new nonlinear estimator which suitable to work with our model.

Keywords: nonlinear systems, state-space model, Kronecker product, nonlinear estimator

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12659 Discrete Choice Modeling in Education: Evaluating Early Childhood Educators’ Practices

Authors: Michalis Linardakis, Vasilis Grammatikopoulos, Athanasios Gregoriadis, Kalliopi Trouli


Discrete choice models belong to the family of Conjoint analysis that are applied on the preferences of the respondents towards a set of scenarios that describe alternative choices. The scenarios have been pre-designed to cover all the attributes of the alternatives that may affect the choices. In this study, we examine how preschool educators integrate physical activities into their everyday teaching practices through the use of discrete choice models. One of the advantages of discrete choice models compared to other more traditional data collection methods (e.g. questionnaires and interviews that use ratings) is that the respondent is called to select among competitive and realistic alternatives, rather than objectively rate each attribute that the alternatives may have. We present the effort to construct and choose representative attributes that would cover all possible choices of the respondents, and the scenarios that have arisen. For the purposes of the study, we used a sample of 50 preschool educators in Greece that responded to 4 scenarios (from the total of 16 scenarios that the orthogonal design resulted), with each scenario having three alternative teaching practices. Seven attributes of the alternatives were used in the scenarios. For the analysis of the data, we used multinomial logit model with random effects, multinomial probit model and generalized mixed logit model. The conclusions drawn from the estimated parameters of the models are discussed.

Keywords: conjoint analysis, discrete choice models, educational data, multivariate statistical analysis

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12658 Financial Fraud Prediction for Russian Non-Public Firms Using Relational Data

Authors: Natalia Feruleva


The goal of this paper is to develop the fraud risk assessment model basing on both relational and financial data and test the impact of the relationships between Russian non-public companies on the likelihood of financial fraud commitment. Relationships mean various linkages between companies such as parent-subsidiary relationship and person-related relationships. These linkages may provide additional opportunities for committing fraud. Person-related relationships appear when firms share a director, or the director owns another firm. The number of companies belongs to CEO and managed by CEO, the number of subsidiaries was calculated to measure the relationships. Moreover, the dummy variable describing the existence of parent company was also included in model. Control variables such as financial leverage and return on assets were also implemented because they describe the motivating factors of fraud. To check the hypotheses about the influence of the chosen parameters on the likelihood of financial fraud, information about person-related relationships between companies, existence of parent company and subsidiaries, profitability and the level of debt was collected. The resulting sample consists of 160 Russian non-public firms. The sample includes 80 fraudsters and 80 non-fraudsters operating in 2006-2017. The dependent variable is dichotomous, and it takes the value 1 if the firm is engaged in financial crime, otherwise 0. Employing probit model, it was revealed that the number of companies which belong to CEO of the firm or managed by CEO has significant impact on the likelihood of financial fraud. The results obtained indicate that the more companies are affiliated with the CEO, the higher the likelihood that the company will be involved in financial crime. The forecast accuracy of the model is about is 80%. Thus, the model basing on both relational and financial data gives high level of forecast accuracy.

Keywords: financial fraud, fraud prediction, non-public companies, regression analysis, relational data

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12657 Predisposition of Small Scale Businesses in Fagge, Kano State, Nigeria, Towards Profit and Loss Sharing Mode of Finance

Authors: Farida, M. Shehu, Shehu U. R. Aliyu


Access to finance has been recognized in the literature as one of the major impediments confronting small scale businesses (SSBs). This largely arises due to high lending rate, religious inclinations, collateral, etc. Islamic mode finance operates under Profit and Loss Sharing (PLS) arrangement between a borrower (business owner) and a lender (Islamic bank). This paper empirically assesses the determinants of predisposition of small scale business operators in Fagge local government area, Kano State, Nigeria, towards the PLS. Cross-sectional data from a sample of 291 small scale business operators was analyzed using logit and probit regression models. Empirical results reveal that while awareness and religion inclination positively drive interest towards the PLS, lending rate and collateral work against it. The paper, therefore, strongly recommends more advocacy campaigns and setting up of more Islamic banks in the country to cater for the financing and religious needs of SSBs in the study area.

Keywords: Islamic finance, logit and probit models, profit and loss sharing small scale businesses, finance, commerce

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12656 The Role of Institutional Quality and Institutional Quality Distance on Trade: The Case of Agricultural Trade within the Southern African Development Community Region

Authors: Kgolagano Mpejane


The study applies a New Institutional Economics (NIE) analytical framework to trade in developing economies by assessing the impacts of institutional quality and institutional quality distance on agricultural trade using a panel data of 15 Southern African Development Community (SADC) countries from the years 1991-2010. The issue of institutions on agricultural trade has not been accorded the necessary attention in the literature, particularly in developing economies. Therefore, the paper empirically tests the gravity model of international trade by measuring the impact of political, economic and legal institutions on intra SADC agricultural trade. The gravity model is noted for its exploratory power and strong theoretical foundation. However, the model has statistical shortcomings in dealing with zero trade values and heteroscedasticity residuals leading to biased results. Therefore, this study employs a two stage Heckman selection model with a Probit equation to estimate the influence of institutions on agricultural trade. The selection stages include the inverse Mills ratio to account for the variable bias of the gravity model. The Heckman model accounts for zero trade values and is robust in the presence of heteroscedasticity. The empirical results of the study support the NIE theory premise that institutions matter in trade. The results demonstrate that institutions determine bilateral agricultural trade on different margins with political institutions having positive and significant influence on bilateral agricultural trade flows within the SADC region. Legal and economic institutions have significant and negative effects on SADC trade. Furthermore, the results of this study confirm that institutional quality distance influences agricultural trade. Legal and political institutional distance have a positive and significant influence on bilateral agricultural trade while the influence of economic, institutional quality is negative and insignificant. The results imply that nontrade barriers, in the form of institutional quality and institutional quality distance, are significant factors limiting intra SADC agricultural trade. Therefore, gains from intra SADC agricultural trade can be attained through the improvement of institutions within the region.

Keywords: agricultural trade, institutions, gravity model, SADC

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12655 Hedging and Corporate Governance: Lessons from the Financial Crisis

Authors: Rodrigo Zeidan


The paper identifies failures of decision making and corporate governance that allow non-financial companies around the world to develop hedging strategies that lead to hefty losses in the aftermath of the financial crisis. The sample is comprised of 346 companies from 10 international markets, of which 49 companies (and a subsample of 13 distressed companies) lose a combined US$18.9 billion. An event study shows that most companies that present losses in derivatives experience negative abnormal returns, including a number of companies in which the effect is persistent after a year. The results of a probit model indicate that the lack of a formal hedging policy, no monitoring to the CFOs, and considerations of hubris and remuneration contribute to the mismanagement of hedging policies.

Keywords: risk management, hedging, derivatives, monitoring, corporate governance structure, event study, hubris

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12654 Logistic Regression Model versus Additive Model for Recurrent Event Data

Authors: Entisar A. Elgmati


Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model.

Keywords: additive model, cumulative probabilities, infant diarrhoea, recurrent event

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12653 The Effect of Aromatherapy Candle as Insecticide from Citrus Extract of Lemongrass (Cymbopogon) to Increase Ae. aegypti Mortality

Authors: Nurul Hidayah, Farida Rahmatika, Fathimah Azzahra, Nesty Herennadia


Aromatherapy candles are one of the insecticide media that have not been much researched. The active ingredient that is proven to have the effect of insecticide is a citrus extract from lemongrass oil (Cymbopogon). Aromatherapy candles are added by citrus compounds to be insecticidal for Ae. aegypti mosquito that was related to the infectious disease such as dengue fever. This research aims to find out if aromatherapy candles of citrus compounds have an insecticidal effect on Ae. aegypti mosquito. We used true experimental design including posttest only with control group design. The samples are 20 male and female Ae. aegypti mosquitos with aged 1-7 days belong to the inclusion criteria. The subjects were divided into 6 groups, consisting of 1 negative control group and 5 treatment groups with variation concentration are 1%; 2%; 3%; 4%; 5%. Each group will be treated for 2 hours and observed death after 24 hours. Replication in each group is done 4 times. The results were then tested statistically using Kruskal-Wallis and probit test. Mean of death in negative control group, and treatment group 1%; 2%; 3%; 4%; 5% respectively 0; 1; 0.25; 0; 1 and 1 mosquito. The Kruskal-Wallis test in the study group found no significant difference (p = 0.178). The probit analysis showed that LC50 and LC90 were 20.069% and 31.557%. The aromatherapy candle of a citrus compound has an insecticidal effect on the Ae aegypti mosquito.

Keywords: Ae. aegypti insecticide, aromatherapy candle, citrus compound, lemongrass oil (Cymbopogon)

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12652 Importance of Health and Social Capital to Employment Status of Indigenous Peoples in Canada

Authors: Belayet Hossain, Laura Lamb


The study investigates the importance of health and social capital in determining the labour force status of Canada’s Indigenous population using data from 2006 Aboriginal Peoples Survey. An instrumental variable ordered probit model has been specified and estimated. The study finds that health status and social capital are important in determining Indigenous peoples’ employment status along with other factors. The results of the study imply that human resource development initiatives of Indigenous Peoples need to be broadened by including health status and social capital. Poor health and low degree of inclusion of the Indigenous Peoples need to be addressed in order to improve employment status of Canada’s Indigenous Peoples.

Keywords: labour force, human capital, social capital, aboriginal people, Canada

Procedia PDF Downloads 164
12651 Mathematical Model to Quantify the Phenomenon of Democracy

Authors: Mechlouch Ridha Fethi


This paper presents a recent mathematical model in political sciences concerning democracy. The model is represented by a logarithmic equation linking the Relative Index of Democracy (RID) to Participation Ratio (PR). Firstly the meanings of the different parameters of the model were presented; and the variation curve of the RID according to PR with different critical areas was discussed. Secondly, the model was applied to a virtual group where we show that the model can be applied depending on the gender. Thirdly, it was observed that the model can be extended to different language models of democracy and that little use to assess the state of democracy for some International organizations like UNO.

Keywords: democracy, mathematic, modelization, quantification

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12650 The Achievement Model of University Social Responsibility

Authors: Le Kang


On the research question of 'how to achieve USR', this contribution reflects the concept of university social responsibility, identify three achievement models of USR as the society - diversified model, the university-cooperation model, the government - compound model, also conduct a case study to explore characteristics of Chinese achievement model of USR. The contribution concludes with discussion of how the university, government and society balance demands and roles, make necessarily strategic adjustment and innovative approach to repair the shortcomings of each achievement model.

Keywords: modern university, USR, achievement model, compound model

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12649 Weighing the Economic Cost of Illness Due to Dysentery and Cholera Triggered by Poor Sanitation in Rural Faisalabad, Pakistan

Authors: Syed Asif Ali Naqvi, Muhammad Azeem Tufail


Inadequate sanitation causes direct costs of treating illnesses and loss of income through reduced productivity. This study estimated the economic cost of health (ECH) due to poor sanitation and factors determining the lack of access to latrine for the rural, backward hamlets and slums of district Faisalabad, Pakistan. Cross sectional data were collected and analyzed for the study. As the population under study was homogenous in nature, it is why a simple random sampling technique was used for the collection of data. Data of 440 households from 4 tehsils were gathered. The ordinary least square (OLS) model was used for health cost analysis, and the Probit regression model was employed for determining the factors responsible for inaccess to toilets. The results of the study showed that condition of toilets, situation of sewerage system, access to adequate sanitation, Cholera, diarrhea and dysentery, Water and Sanitation Agency (WASA) maintenance, source of medical treatment can plausibly have a significant connection with the dependent variable. Outcomes of the second model showed that the variables of education, family system, age, and type of dwelling have positive and significant sway with the dependent variable. Variable of age depicted an insignificant association with access to toilets. Variable of monetary expenses would negatively influence the dependent variable. Findings revealed the fact, health risks are often exacerbated by inadequate sanitation, and ultimately, the cost on health also surges. Public and community toilets for youths and social campaigning are suggested for public policy.

Keywords: sanitation, toilet, economic cost of health, water, Punjab

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12648 Model Averaging for Poisson Regression

Authors: Zhou Jianhong


Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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