scholarly journals Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3307
Author(s):  
Jorge M. Bravo ◽  
Mercedes Ayuso
Keyword(s):  
Life Expectancy ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Stochastic Mortality ◽  
Policy Responses ◽  
Working Lives ◽  
Aging Populations ◽  
Factor Design ◽  
Pension Adequacy

Linking pensions to longevity developments at retirement age has been one of the most common policy responses to pension schemes and aging populations. The introduction of automatic stabilizers is primarily motivated by cost containment objectives, but there are other dimensions of welfare restructuring in the politics of pension reforms, including recalibration, rationalization, and blame avoidance for unpopular policies that involve retrenchments. This paper examines the policy designs and implications of linking entry pensions to life expectancy developments through sustainability factors or life expectancy coefficients in Finland, Portugal, and Spain. To address conceptual and specification uncertainty in policymaking, we propose and apply a Bayesian model averaging approach to stochastic mortality modeling and life expectancy computation. The results show that: (i) sustainability factors will generate substantial pension entitlement reductions in the three countries analyzed; (ii) the magnitude of the pension losses depends on the factor design; (iii) to offset pension cuts and safeguard pension adequacy, individuals will have to prolong their working lives significantly; (iv) factor designs considering cohort longevity markers would have generated higher pension cuts in countries with increasing life expectancy gap.

scholarly journals Human Capital and Economic Growth in Morocco: Evidence from Bayesian Model Averaging

2017 ◽  
Vol 10 (12) ◽  
pp. 167 ◽  
Author(s):  
Omar Essardi ◽  
Redouane Razzouk
Keyword(s):  
Economic Growth ◽  
Human Capital ◽  
Life Expectancy ◽  
Granger Causality ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Granger Causality Test ◽  
Causality Test

The paper investigates the relationship between human capital and economic growth in Morocco during the period from 1965 to 2015. In order to test this relationship we estimated a growth function using firstly the Johansen multivariate cointegration test and the Granger causality test. Secondly, we used the method of the Bayesian Model Averaging (BMA) that takes into consideration the uncertainty related to the specification of the model studied. In the theoretical literature, the difficulty of measuring human capital is often stressed. In order to overcome this problem, we use four proxies of human capital: first, we employ the average years of schooling. Second, we use the index of the gap in life expectancy between Morocco and developed countries. Third, we integrate the qualitative aspects of education and health by constructing two composite indicators of human capital using Principal Component Analysis (PCA) method.The main results of regression analysis confirm that in the specification of determinants of GDP per worker the average years of total schooling, the life expectancy index and the indicator of quality of health affect positively and significantly level of GDP per worker. However, in the specification of determinants of the growth of the GDP per worker, we found there is no proxy of human capital that affects significantly the growth of the GDP per worker.In addition, the results of Granger causality test show that only the indicator of quality of health that cause the GDP per worker. As well, these results show that the average years of total schooling and the indicator of quality of education cause the growth of GDP per worker. We suggest that the Moroccan authorities should make additional efforts to raise the level of quality of human capital especially in the health sector and increase the productivity of both public and private investment.

scholarly journals Factors Affecting Life Expectancy in East Java: Predictions with A Bayesian Model Averaging Approach

2021 ◽  
Vol 5 (2) ◽  
pp. 283-295
Author(s):  
Harun Al Azies ◽  
Vivi Mentari Dewi
Keyword(s):  
Life Expectancy ◽  
Regression Model ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Infant Mortality Rate ◽  
Model Parameters ◽  
Data Set ◽  
Expectancy Value ◽  
Factors Affecting

This study predicts the factors that influence life expectancy in East Java, Indonesia. In particular, this study compares the prediction results between the linear regression model and the Bayesian Model Averaging (BMA). The study used a 2015 data set from the Central Bureau of Statistics (BPS) of the province of East Java.The results of data exploration show that the life expectancy in East Java is 70.68 years, the Bondowoso regency is the region with the lowest life expectancy at 65.73 years and the city of Surabaya is the area with the highest life expectancy value in East Java, which is 73.85 years.The results of the inference study indicate that the factors that are expected to affect life expectancy in East Java are the infant mortality rate and the illiteracy rate of the population aged 10 and over.The results of the comparison between the BMA and the regression show that the BMA is a better model for predicting the factors that affect life expectancy in East Java than the regression model because the BMA model can estimate the parameters more efficiently by estimating the model parameters based on the standard error value.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

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