The article focuses on the housing market, the behavior and motivations of senior households to move or to stay in place. Knowing if and why seniors decide to move at retirement is a critical factor for the establishment of social service policies in terms of their structure, location, and provision.
This study uses secondary data based on information about Czech households collected by the Czech Statistical Office (CSO). The data are annually collected via sample surveys of the income and living conditions of households (EU-SILC). The sample covers more than eight thousands of households. Analyzed data cover the period 2007–2012 when the abolishment of rent regulation in the Czech Republic took place. It is hypothesized that an impact like this might increase the willingness to move and reveal the factors which underlie the decisions of particular households.
The results indicated that most Czech households that decided to move during the study period were driven by the increased financial burden of housing. Other factors, including the availability of social services and public utilities within the current location, played only minor roles. It seems that Czech senior households act in a very pragmatic and rational manner when deciding whether to stay in place or move, with the majority of households preferring not to move. Social policies should, therefore, concentrate on providing services for the current locations rather than on the construction of new social housing. 相似文献
This study examines the association between providing care for grandchildren and the economic status of grandparents, focusing on the employment status. This study asks two questions. First, is providing care for grandchildren related to Korean grandparents’ employment status? Second, are the intensities of providing care for grandchildren related to grandparents’ employment status? In examining these research questions, this study focuses on gender and caregiving intensity. The findings suggest that providing care for grandchildren was associated with Korean grandmothers’ employment status. In addition, there are different relationships between providing care for grandchildren and grandparents’ employment status according to the caregiving intensities. 相似文献
The proportional odds model (POM) is commonly used in regression analysis to predict the outcome for an ordinal response variable. The maximum likelihood estimation (MLE) approach is typically used to obtain the parameter estimates. The likelihood estimates do not exist when the number of parameters, p, is greater than the number of observations n. The MLE also does not exist if there are no overlapping observations in the data. In a situation where the number of parameters is less than the sample size but p is approaching to n, the likelihood estimates may not exist, and if they exist they may have quite large standard errors. An estimation method is proposed to address the last two issues, i.e. complete separation and the case when p approaches n, but not the case when p>n. The proposed method does not use any penalty term but uses pseudo-observations to regularize the observed responses by downgrading their effect so that they become close to the underlying probabilities. The estimates can be computed easily with all commonly used statistical packages supporting the fitting of POMs with weights. Estimates are compared with MLE in a simulation study and an application to the real data. 相似文献
Abstract. Family‐based case–control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to explore the situation of measurement error affecting the assessment of the environmental exposure. We correct for measurement error through a likelihood‐based method. We exploit a conditional likelihood approach to relate the probability of disease to the genetic and the environmental risk factors. We show that this approach provides less biased and more efficient results than that based on logistic regression. Regression calibration, instead, provides severely biased estimators of the parameters. The comparison of the correction methods is performed through simulation, under common measurement error structures. 相似文献