Guillermo Rodríguez, Ana Serrano, Miguel Martín-Retortillo, Ignacio Cazcarro (p)

Les Dolega

The article consists of the analysis and evaluation of the influencing factors in the evolution of the (~8000) Spanish municipalities population, from the 20th century to the present. It also evaluates the implications that may derive from this, especially in the face of demographic challenges, such as -mainly rural- depopulation. This implies filling a great gap in terms of systematic and consistent databases and associated literature available. The paper is framed in a research project consisting of 3 specific objectives: 1) Preparation and analysis by Spanish municipalities of a historical database of the population, irrigated areas, dry land and pastures, as well as associated climatic and geographical variables during the 20th century until the present. 2) Preparation and analysis of variables that quantify Spanish municipal economic activity during the 20th century to the present. 3) Analysis and evaluation of explanatory factors of municipal demographic change in Spain, with emphasis on the role of irrigation, as well as the installation of other industries and developments in the long term.
This evaluation, based on statistical and econometric analysis, allows to better understand the main explanatory factors of demographic change at such a fine scale, and potentially project development policies on the territories accordingly. The study has a clearly applied regional economics and historical character, being projected to be useful for the present and the future. Although the population information by municipality is relatively accessible from the censuses (~every decade), there is a lack of coherent and unified information for holistic analyses. A good part of the climatic, geographic and land use information is gathered and elaborated by the authors, being other variables potentially explanatory of population change less obvious or accessible. This fact is especially noticeable for economic variables on a scale as specific as municipalities. It can be stated that there are no databases or studies that have been capable of systematically representing and analysing the economic evolution of Spanish municipalities. Therefore, until now, it has not been possible to quantitatively evaluate jointly the population change with a clear-cut economics variable at municipal level, nor with other potentially explanatory factors, as it is being done in this work, which pioneers it via employment at detailed sectoral level. The work is intended also to provide example for other methodological and empirical works at European Union at fine scale, e.g. on the economics and geographical variables elaboration and joint analysis with demographics.

Xiuxiang Pan (p), Dimitris Ballas, Sierdjan Koster

Ignacio Cazcarro

The "drifting elderly"(or trailing elderly) in China is composed of elder parents who migrate with their adult children from rural to urban in their later life. Due to China’s rural-urban dual system and urbanization, this special migration group has recently been growing rapidly. They move to the urban area where their adult children live to provide family support (e.g., taking care of grandchildren and doing housework). The migration pattern has been examined with a focus on the great contribution to the work-family balance of the young generations. However, there is a paucity of knowledge on the impact of this migration on the happiness and well-being of these elder parents. There has been evidence showing the impact of social integration and improvement of quality of life on the happiness of migrants. However, how the factors affect drifting elderly’s happiness remain unclear. This study intends to fill in this gap.
We will explore the following hypotheses. First, we assume spatial inequality in the happy ratio of elderly migrants (HREM). Particularly, the HREM is higher in southeast provinces than in other regions of China. Second, we hypothesize that the improvement of quality of life positively impacts the elderly’s happiness while the lack of social integration shows a negative impact. Third, we have the hypothesis that there is a significant interaction effect of social integration and the quality-of-life related factors on affecting drifting elderly’s happiness.
Using the data from 2015 and 2018 China Health and Retirement Longitudinal Study (CHARLS), we will first conduct a spatial analysis to explore the geographical patterns of HREM in China. Then a logistic model will be employed to explore the impact of social integration and quality-of-life related factors on drifting elderly's happiness as well as their interaction effect. The potential contribution of this study is as follows. First, by empirically examining the role of social integration and quality-of-life-related factors, we shed new light on the happiness study of a special group in contemporary China (i.e., the drifting elderly). Second, our expected findings may suggest that social integration and quality of life both are important for the happiness of the drifting elderly. Therefore a more inclusive social policy should be adopted for improving the special group’s happiness.

Les Dolega (p)

Xiuxiang Pan

Abstract
The English population is rapidly aging. A granular understanding of the key characteristics and spatial distribution of this population is required to tackle its emerging needs. To better understand the social and spatial heterogeneity within the older population and thereby support effective policy development and targeted service provision, this study develops an open access, multidimensional classification of the older population in England at a small area level.

The ‘Aging in Place Classification’ (AiPC) geodemographic classification has been designed to investigate the conditions of ageing population across 9 key domains. It pertains to population aged 50+ and employs cross-sectional data including Census and other novel data sources capturing housing, health, digital engagement, mobility and consumer behaviour characteristics. The classification is built using multiple machine learning techniques and it has been validated through a ground-truthing process. A top-down k-means clustering model grouped areas at the LSOA level into two tiers: 5 main clusters (Supergroups) and 13 nested sub-clusters (Groups). By creating the so-called ‘Pen Portraits’, we capture key characteristics of each cluster and sub-cluster. Currently, we aim to demonstrate how AiPC can be applied to better understand the issue of loneliness and to inform more targeted interventions to improving and readapting housing stock and how new service provisions for older people can be better integrated at a neighbourhood level. The bespoke geodemographic classification, our analysis and data products provide invaluable insights enabling more effective planning and policy intervention that address the needs and opportunities arising from an older population.