We would like to welcome you all to the CMIST PhD lunchtime seminar, on Tuesday October 20th from 12 pm lasting until approximately 13 pm in room 2.07 (Humanities Bridgeford street), where we will listen to talks by Bo Hou and Angelo Moretti (abstracts below).
light refreshments are provided.
Everyone is welcome!
Impacts of Urbanization on Health and Well-being in Later Life in China- Evidence from the China Health and Retirement Longitudinal Study (CHARLS)
Unprecedented urbanization has taken place in China over the past several decades; the percentage of the Chinese population lives in urban areas has increased from 17.9% in 1978 to 51.3% in 2011, and the Chinese government aims to raise this to 70% by 2025. Thus the sheer size of urbanization and the implications for population health and future public policy make the study of impacts of urbanization on health and well-being very relevant in China.
Studies on urbanization and health in developing countries have found that urbanization is associated with environmental damages and unhealthy diet and lifestyle. In this paper, we investigate the long-term impacts of urbanization on health and well-being in China through comparing health outcomes of in-situ urbanized rural residents with both rural and urban stayers. To do that, we use the China Health and Retirement Longitudinal study, a national representative dataset.
Our results show that there is a consistent health and mental health advantage of in-situ urbanised rural residents compared with the rural stayers, and hardly any of the control variables that reflect the social determinants of health attenuate this effect. Furthermore, this is a first study to examine the impacts of urbanization independent of migration in China.
Small Area Estimation Methods for Multidimensional Indicators: a Model-based Experiment
Measuring poverty and well-being is a key issue for governments and policy makers who require a detailed understanding of the geographical distribution of social indicators (e.g means, ratio, proportions). This understanding is essential for the formulation of targeted policies that address the need of people in specific geographical locations.
Most large-scale social sample surveys provide accurate estimates at regional levels. For instance, a relevant survey in the European Union for analysing social exclusion phenomena is EU-SILC (European Union Statistics for Income and Living Conditions). However, this data can be used to produce accurate direct estimates only at the NUTS 2 (Nomenclature of Territorial Units for Statistics) level. Hence, when the goal is to measure poverty and well-being indicators at a sub-regional level, they cannot be directly estimated from EU-SILC (Pratesi et al., 2013). Thus, indirect estimation methods, in particular small area estimation (SAE) methods should be used in that case.
It is generally agreed that poverty and well-being are multidimensional phenomena. Therefore, multivariate mixed effects models play a crucial role. On the one hand, we can take into account the correlation structure between variables, and on the other hand, we can obtain more accurate and more efficient estimates than the univariate case in SAE (Datta et al., 1999).
In this presentation I will discuss the problem of small area estimation for multidimensional poverty and well-being indicators. Then, I will present the results of some preliminary simulation studies. In the simulation experiments, the population has been generated from the multivariate mixed effects model (Fay and Fuller, 1987). As a first approach I will be investigating a univariate EBLUP (empirical best linear unbiased predictor) (Rao, 2003) for factor scores following a data reduction method of factor analysis. In the simulations I compare the EBLUPs with the direct estimates (obtained via the Horvitz and Thompson estimator) of the mean factor score in small areas. Also, some potential applications on EU-SILC data will be discussed.
Keywords: Factor Analysis; EBLUP; Multivariate Mixed Effects Model; Poverty and Well-being.