scholarly journals An Inquiry into Child Health and its Determinants: Application of Ordered Probit Model

2018 ◽  
Vol 41 (1-2) ◽  
pp. 35-53
Author(s):  
Nirmal Kumar Raut ◽  
Subodh Kumar Sah Karmayogi
Keyword(s):  
Health Status ◽  
Child Health ◽  
Health Service ◽  
Adolescent Health ◽  
Probit Model ◽  
Regional Disparity ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Parental Health ◽  
Parental Health Status

This study analyzes determinants of child health 0 to 5 years old and compares those with the determinants of adolescent health 10 to19 years old. The particular focus is to test whether there exists socio-economic inequality and regional disparity in health of children and adolescents. It utilizes third wave of Nepal Living Standard Survey (NLSS-III) and further exploit ordered probit model to analyze the determinants. The results show the absence of socio-economic inequality in child health while sits presence in adolescent health. It is argued that factors other than income or socio-economic status like medical care, child rearing and breastfeeding matter for child health. However, the study finds regional disparity more pronounced in the health of children than adolescents. This difference may be attributed to the low-level of child-centered health awareness, high cost of access to health service facilities, and limited health service facilities specializing on child health in some regions. In addition, the study finds that caste, age of the child, and parental health status as major determinants of child health while age of the adolescent, education, and their parental health status as major determinants of adolescent health. Further, disaggregation of parental health status reveals that mother's health status matter more. Finally, some recommendations are made for improving child health.

Pattern discovery of health curves using an ordered probit model with Bayesian smoothing and functional principal component analysis

2020 ◽  
pp. 096228022095183
Author(s):  
Shijia Wang ◽  
Yunlong Nie ◽  
Jason M Sutherland ◽  
Liangliang Wang
Keyword(s):  
Principal Component Analysis ◽  
Health Status ◽  
Principal Component ◽  
Probit Model ◽  
Component Analysis ◽  
Ordered Probit ◽  
Functional Principal Component Analysis ◽  
Ordered Probit Model ◽  
Functional Principal Component ◽  
Bayesian Smoothing

This article is motivated by the need for discovering patterns of patients’ health based on their daily settings of care to aid the health policy-makers to improve the effectiveness of distributing funding for health services. The hidden process of one’s health status is assumed to be a continuous smooth function, called the health curve, ranging from perfectly healthy to dead. The health curves are linked to the categorical setting of care using an ordered probit model and are inferred through Bayesian smoothing. The challenges include the nontrivial constraints on the lower bound of the health status (death) and on the model parameters to ensure model identifiability. We use the Markov chain Monte Carlo method to estimate the parameters and health curves. The functional principal component analysis is applied to the patients’ estimated health curves to discover common health patterns. The proposed method is demonstrated through an application to patients hospitalized from strokes in Ontario. Whilst this paper focuses on the method’s application to a health care problem, the proposed model and its implementation have the potential to be applied to many application domains in which the response variable is ordinal and there is a hidden process. Our implementation is available at https://github.com/liangliangwangsfu/healthCurveCode .

Do the heterogeneous determinants of life satisfaction affect differently across borrowers of diverse credit sources? A propensity score approach

2018 ◽  
Vol 45 (8) ◽  
pp. 1142-1158 ◽  
Author(s):  
Tiken Das ◽  
Manesh Choubey
Keyword(s):  
Life Satisfaction ◽  
Propensity Score ◽  
Probit Model ◽  
Ordered Probit ◽  
Primary Data ◽  
Ordered Probit Model ◽  
Future Research ◽  
Content Type ◽  
Credit Access ◽  
Physical Assets

Purpose The purpose of this paper is to evaluate the non-monetary effect of credit access by providing an econometric framework which controls the problem of selection bias. Design/methodology/approach The study is conducted in Assam, India and uses a quasi-experiment design to gather primary data. The ordered probit model is used to evaluate the non-monetary impact of credit access. The paper uses a propensity score approach to check the robustness of the ordered probit model. Findings The study confirms the positive association of credit access to life satisfaction of borrowers. It is found that, in general, rural borrower’s life satisfaction is influenced by the ability and capacity to work, the value of physical assets of the borrowers as well as some other lenders’ and borrowers’ specific factors. But, the direction of causality of the factors influencing borrowers’ life satisfaction is remarkably different across credit sources. Research limitations/implications The study argues to provide productive investment opportunities to semiformal and informal borrowers while improving their life satisfaction score. Although the results are adjusted for selection and survivorship biases, it is impossible with the available data to assess which non-income factors explain the findings, and therefore this limitation is left to future research. Originality/value The study contributes to the literature of rural credit by assessing the probable differences among formal, semiformal and informal credit sources with respect to non-monetary impacts.

scholarly journals The Organization of Political Violence by Insurgencies

2012 ◽  
Vol 18 (3) ◽  
Author(s):  
Roos Haer
Keyword(s):  
Political Violence ◽  
Probit Model ◽  
Ordered Probit ◽  
Process Theory ◽  
Ordered Probit Model ◽  
Armed Groups ◽  
Human Right ◽  
Armed Group ◽  
The World ◽  
The Relationship

AbstractA range of theories have attempted to explain the variation in civilian abuse of warring parties. Most of these theories have been focused on the strategic environment in which these acts take place. Less attention is devoted to the perpetrators of these human right abuses themselves: the armed groups. This study tries to fill this niche by using the organizational process theory in which it is assumed that armed groups, like every organization, struggles for survival. The leader tries to ensure the maintenance of her armed group by increasing her control over her troops. The relationship between the level of control and the perpetrated civilian abuse is examined with a new dataset on the internal structure of more than 70 different armed groups around the world. With the help of a Bayesian Ordered Probit model, this new dataset on civilian abuse is analyzed. The results show that especially particular incentives play an important role.

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