The Regional Development Effects Module (RDEM) will map the impact of migration on regional development seen on different variables. To construct the RDEM we have to:1. develop a typology of regions, based on the impact that mobility has on its economic, social and cultural development; and2. detect the causal linkages between regional mobility on the one hand and regional development on the other.In our presentation we will focus on the process to determine relevant regional development indicators that will help in the collection and analysis of relevant data for the period 2010-2022 on NUTS 2 and 3 level. Partners in our project will additionally focus on:1. Analysis of regional networks estimated from Facebook2. Building typology regional development3. Longitudinal causal analysis of mobility4. Integration of case studiesFinally, this will result in:• Online atlas of mobility & development typologies• Report Causal Analysis of mobility development
Transforming our societies towards a more sustainable future requires a good understanding of their citizens. This is of particular importance when considering the phenomenon of population ageing, which means that older people will constitute a significant share of society. The imperative for sustainable development arises from escalating concerns over environmental issues, necessitating tailored interventions for the heterogeneous group of older individuals. In this research, data collected using the SustainABLE-8 in Poland, North Macedonia, Romania, the Netherlands and Israel (N = 2318) were analysed in order to identify European typologies and their drivers for - and contributions to - sustainable practices. Several items of the SustainABLE-8 concerned (limiting) energy use at home as well as attitudes towards the use of sustainable energy and climate change. The study identified the existence of four major typologies, which differ in terms of their financial position, beliefs and behaviours in relation to the environment. These typologies cover 1) inactive people with limited financial resources, 2) inactive believers, 3) active and belief-driven people with limited financial resources, and 4) active and belief-driven people with financial resources. Each typology is separately discussed in terms of its specificities and ways how local governments could support their pro-environmental behaviours. The research is summarised with practical implications for industry, policymakers and environmental, social and governance strategies.
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Discussions on policy and management initiatives to facilitate individuals throughout working careers take place without sufficient insight into how career paths are changing, how these changes are related to a modernization of life course biographies, and whether this leads to increased labour market transitions. This paper asks how new, flexible labour market patterns can best be analyzed using an empirical, quantitative approach. The data used are from the career module of the Panel Study of Belgian Households (PSBH). This module, completed by almost 4500 respondents consists of retrospective questions tracing lengthy and even entire working life histories. To establish any changes in career patterns over such extended periods of time, we compare two evolving methodologies: Optimal Matching Analysis (OMA) and Latent Class Regression Analysis (LCA). The analyses demonstrate that both methods show promising potential in discerning working life typologies and analyzing sequence trajectories. However, particularities of the methods demonstrate that not all research questions are suitable for each method. The OMA methodology is appropriate when the analysis concentrates on the labour market statuses and is well equipped to make clear and interpretable differentiations if there is relative stability in career paths during the period of observation but not if careers become less stable. Latent Class has the strength of adopting covariates in the clustering allowing for more historically connected types than the other methodology. The clustering is denser and the technique allows for more detailed model fitting controls than OMA. However, when incorporating covariates in a typology, the possibilities of using the typology in later, causal, analyses is somewhat reduced.
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