Household consumption and home production at retirement in Thailand: evidence from a regression discontinuity approach

This paper uses Socio-Economic Surveys covering the period from 2013 to 2019 and the 2015 Time Use Survey to investigate the extent to which household consumption changes at retirement in Thailand. A fuzzy regression discontinuity design is applied to evaluate the retirement effect on total household expenditure and expenditures on four major categories: food-at-home, work-related items, non-durable entertainment, and others. The results reveal that retirement decreases household expenditure by 11%. Further investigations show that the dramatic declines in expenditures on work-related and non-durable entertainment contribute significantly to the spending drop at retirement. The magnitudes of the declines are more pronounced for low-income and low-wealth households. The results also indicate that the retirees spend more leisure time on home production activities after retirement. Once accounting for this effect, it finds that the drop in total household expenditure decreases to 6%. These results suggest that the sizable consumption expenditure drop at retirement is due to substituting away from market purchased goods toward home-produced goods.

Algorithm for detecting outliers in the model uniformly fuzzy regression

When constructing mathematical models based on statistical data, the researcher faces the need to assess the homogeneity of the sample, in particular, the study of data on emissions. Availability in a sample of outliers negatively affects the modeling results and the adequacy of the model as a whole. In this work, an algorithm has been developed that allows one to quantitatively measure the effect of the influence of each observation on the quality of the constructed model. The description of this algorithm is given. Previously the author carried out similar studies for various regression models.

How the Spouse's Retirement Affects the Cognitive Health of Individuals in China: A Fresh Evidence From the Perspective of Social Interaction

Background: With the rapid aging of global population, the health consequences of retirement reform are debated greatly. However, most previous studies are limited to the effects on individual themselves, and pay scant attention to the social interaction between individuals and their spouse which may induce the social multiplier effect of retirement. Driven by the practical and academic motives, this study investigates the impacts of the spouse's retirement on the individual's cognitive health among Chinese dual-earner couples.Methods: We first build a simultaneous-equations model. Then, using the data from the 2010 to 2018 China Family Panel Studies (CFPS), we choose the fixed-effects model and adopt the fuzzy regression discontinuity design method to analyze. Besides, we check the validity and robustness of the results. Finally, we employ the mediating effect model to explore the mechanisms.Results and Conclusions: The spouse's retirement has significantly negative direct and indirect effect on individual cognitive health. Husbands' retirement has a stronger adverse spillover effect than wives' retirement, and wives' cognitive health is more vulnerable to the social interaction effect. The direct spillover effect of husbands' retirement is −0.503 and that of wives' retirement is −0.312, the indirect spillover effect of husbands' retirement is −0.36 and that of wives' retirement is −0.279. In addition to the social interaction effect of cognition between the couples, we also find that the decrease in household income is an important mechanism, and that the increased exercise frequency can somewhat mitigate the adverse spillover effect.

Introducing a trapezoidal interval type-2 fuzzy regression model

The uncertainty is an important attribute about data that can arise from different sources including randomness and fuzziness, therefore in uncertain environments, especially, in modeling, planning, decision-making, and control under uncertainty, most data available contain some degree of fuzziness, randomness, or both, and at the same time, some of this data may be anomalous (outliers). In this regard, the new fuzzy regression approaches by creating a functional relationship between response and explanatory variables can provide efficient tools to explanation, prediction and possibly control of randomness, fuzziness, and outliers in the data obtained from uncertain environments. In the present study, we propose a new two-stage fuzzy linear regression model based on a new interval type-2 (IT2) fuzzy least absolute deviation (FLAD) method so that regression coefficients and dependent variables are trapezoidal IT2 fuzzy numbers and independent variables are crisp. In the first stage, to estimate the IT2 fuzzy regression coefficients and provide an initial model (by original dataset), we introduce two new distance measures for comparison of IT2 fuzzy numbers and propose a novel framework for solving fuzzy mathematical programming problems. In the second stage, we introduce a new procedure to determine the mild and extreme fuzzy outlier cutoffs and apply them to remove the outliers, and then provide the final model based on a clean dataset. Furthermore, to evaluate the performance of the proposed methodology, we introduce and employ suitable goodness of fit indices. Finally, to illustrate the theoretical results of the proposed method and explain how it can be used to derive the regression model with IT2 trapezoidal fuzzy data, as well as compare the performance of the proposed model with some well-known models using training data designed by Tanaka et al. [55], we provide two numerical examples.

Vocational training for unemployed youth in Latvia

We analyze the effectiveness of a vocational training (VT) programme targeting unemployed youth in Latvia, contributing to the scant literature on active labour market policies in transition countries. The programme we analyse is part of the Youth Guarantee scheme (2014–2020), the largest action launched by the European Union to combat youth unemployment after the 2008 financial crisis. Although the programme was targeted to youths aged between 15 and 29, priority was given to those younger than 25 years of age. We exploit this eligibility rule in a fuzzy regression discontinuity design framework to estimate the impact of VT participation on the probability of being employed and gross monthly labour income at given dates after the training. Using rich administrative data, we find that the age priority rule increased programme participation for the youngest group by about 10 percentage points. However, participation in the programme did not lead to statistically significant positive effects in labour market outcomes. We argue that this result could be due to some specific characteristics of the programme, namely the voucher system (potentially inducing lock-in effects) and the type of training (classroom instead of on-the-job training). Moreover, the programme was targeted at ex-ante low-employable individuals (e.g. without vocational qualifications), a fact that is confirmed by our analysis of the characteristics of the population of compliers with the age priority rule.

Optimal Design of Adaptive Neuro-Fuzzy Inference System Using PSO And Ant Colony Optimization For Estimation of Uncertain Observed Values

This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC\({O}_{R}\). In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

Extended Fully Fuzzy Linear Regression to Analyze a Solid Cantilever Beam Moment

There are several procedures such as possibilistic and least-square methods to estimate regression models. In this study, first, a fully fuzzy regression equation is converted into a fully fuzzy linear framework. By considering a least-square approach, a model is suggested based on matrix equations for solving fully fuzzy regression models. The main advantage of this method over existing ones is that this method considered values based on their specification, and all linear problems can be easily solved. Moreover, a case study for solid mechanics about the quantity of beam momentum is considered. In this example, the inner data are force values, and the output is momentum values.