scholarly journals Maternal Mortality in Ghana: Impact of the Fee-Free Delivery Policy and the National Health Insurance Scheme

2015 ◽  
Vol 4 (3) ◽  
pp. 232
Author(s):  
Seidu Sofo ◽  
Emmanuel Thompson
Keyword(s):  
Health Insurance ◽  
Panel Data ◽  
Maternal Mortality ◽  
National Health Insurance ◽  
National Health ◽  
Data Model ◽  
Fixed Effects ◽  
Panel Data Model ◽  
Skilled Delivery ◽  
The Impact

<p>Maternal mortality (MMR) is the second largest cause of female deaths in Ghana. Yet, many households cannot afford the cost of skilled delivery The study utilized the Panel Data Model to examine the impact of the fee-free delivery (FDP) and the National Health Insurance Policy (NIP) exemptions on MMR in Ghana. The Demographic and Health Survey reports on Ghana from 2002 to 2009 served as the main data source. Data were analyzed using Panel data model with within group fixed effects estimator. MMR declined significantly over the period studied. Both FDP and NIP positively impacted MMR at a 5% level of significance. In addition, skilled delivery was a significant predictor of MMR. Stakeholders would do well to ensure NIP is adequately funded in order to sustain the decline in MMR.</p><p> </p><p><strong><br /></strong></p>

Maternal Mortality in Ghana: Impact of the Fee-Free Delivery Policy and the National Health Insurance Scheme

2015 ◽  
Vol 4 (3) ◽  
pp. 232
Author(s):  
Seidu Sofo ◽  
Emmanuel Thompson
Keyword(s):  
Health Insurance ◽  
Panel Data ◽  
Maternal Mortality ◽  
National Health Insurance ◽  
National Health ◽  
Data Model ◽  
Fixed Effects ◽  
Panel Data Model ◽  
Skilled Delivery ◽  
The Impact

<p>Maternal mortality (MMR) is the second largest cause of female deaths in Ghana. Yet, many households cannot afford the cost of skilled delivery The study utilized the Panel Data Model to examine the impact of the fee-free delivery (FDP) and the National Health Insurance Policy (NIP) exemptions on MMR in Ghana. The Demographic and Health Survey reports on Ghana from 2002 to 2009 served as the main data source. Data were analyzed using Panel data model with within group fixed effects estimator. MMR declined significantly over the period studied. Both FDP and NIP positively impacted MMR at a 5% level of significance. In addition, skilled delivery was a significant predictor of MMR. Stakeholders would do well to ensure NIP is adequately funded in order to sustain the decline in MMR.</p><p> </p><p><strong><br /></strong></p>

scholarly journals Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

2018 ◽  
Vol 11 (3) ◽  
pp. 44 ◽  
Author(s):  
Karen Yan ◽  
Qi Li
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Numerical Optimization ◽  
Fixed Effects ◽  
Quantile Function ◽  
Panel Data Model ◽  
Nonparametric Method ◽  
Conditional Quantile ◽  
Finite Samples ◽  
Conditional Quantile Function

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples.

The impact of national health insurance on the volume and severity of emergency department use

1998 ◽  
Vol 16 (1) ◽  
pp. 92-94 ◽  
Author(s):  
Yeong-Liang Lin ◽  
Chuhsing Kate Hsiao ◽  
Huel-Ming Ma ◽  
Hong-Yuan Hsu ◽  
Shih-Ming Wang ◽  
...  
Keyword(s):  
Emergency Department ◽  
Health Insurance ◽  
National Health Insurance ◽  
National Health ◽  
Emergency Department Use ◽  
The Impact

Infrastructural Development

2017 ◽  
pp. 1-22 ◽  
Author(s):  
Maniklal Adhikary ◽  
Melisha Khatun
Keyword(s):  
Panel Data ◽  
Gross Domestic Product ◽  
Data Model ◽  
Electricity Production ◽  
Panel Data Model ◽  
Domestic Product ◽  
Rate Of Growth ◽  
Internet Users ◽  
Per Capita ◽  
The Impact

Development of infrastructure industries is essential to enhance the growth of a developing country. The present chapter attempts to examine the impact of infrastructure on Gross Domestic Product and Per Capita Gross Domestic Product of six SAARC countries from the period 1990-91 to 2013-14. The model is mis-specified whenever we have used the restricted panel data model. We have derived the results by employing the unrestricted panel data model. Impact of road, internet users and total electricity production on the level of GDP as well as on the level of PCGDP is highest for India among the all SAARC countries. India has also the highest rate of growth of GDP over the entire period. Rate of growth of PCGDP is highest for Sri Lanka followed by India.

scholarly journals Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model

2020 ◽  
Vol 24 (21) ◽  
pp. 15937-15949
Author(s):  
Giorgio Gnecco ◽  
Federico Nutarelli ◽  
Daniela Selvi
Keyword(s):  
Panel Data ◽  
Sample Size ◽  
Upper Bound ◽  
Data Model ◽  
Fixed Effects ◽  
Conditional Variance ◽  
Panel Data Model ◽  
Generalization Error ◽  
Trade Off ◽  
Selection Probabilities

Abstract This paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional possibility of controlling the conditional variance of the output given the input and the selection probabilities of the different units per unit time. This is achieved by varying the cost associated with the supervision of each training example. Assuming an upper bound on the expected total supervision cost and fixing the expected number of observed units for each instant, we analyze and optimize the trade-off between sample size, precision of supervision (the reciprocal of the conditional variance of the output) and selection probabilities. This is obtained by formulating and solving a suitable optimization problem. The formulation of such a problem is based on a large-sample upper bound on the generalization error associated with the estimates of the parameters of the unbalanced fixed effects panel data model, conditioned on the training input dataset. We prove that, under appropriate assumptions, in some cases “many but bad” examples provide a smaller large-sample upper bound on the conditional generalization error than “few but good” ones, whereas in other cases the opposite occurs. We conclude discussing possible applications of the presented results, and extensions of the proposed optimization framework to other panel data models.

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