Aim: To break new ground in the field of official statistics, COMBINE produces error-corrected figures on (1) the number of individuals receiving social assistance benefit at a given point in time and (2) the transition rate out of social assistance between two points in time. Background: Social assistance benefits are an important pillar of the welfare state. Policy making on social assistance benefits is based on official statistics that are produced by National Statistical Institutes with the use of register data. However, register data suffer from measurement error that can severely bias official statistics and, consequently, policy-making that uses these statistics. Innovation: COMBINE tests, applies and evaluates a novel model-based error-detection and correction method on social assistance benefits. This method does not require concrete knowledge of the causes of measurement error. With this method, we produce different and more reliable official statistics on social assistance benefits. Moreover, this method is more efficient than the practices of data-quality improvement that National Statistical Institutes currently apply. Plan: The project is carried out in collaboration with Statistics Netherlands and, in particular, with its Department of Demographic and Socioeconomic Statistics. Besides producing error-corrected official statistics on social assistance benefits, COMBINE delivers an implementation plan of the method to other official statistics (by Statistics Netherlands or other National Statistical Institutes) and a dissemination plan for approaching policymakers and other users of the statistics. Impact: COMBINE will encourage a cultural shift in official statistics by boosting the confidence of National Statistical Institutes in producing model-based error-corrected statistics. In the policy field, as the method is applicable to a wide range of topics, the project contributes to evidence-based policy-making.

One of the important consequences of the Second Demographic Transition has been the increasing complexity of families. The aim of this project is to study how rising family complexity has affected two fundamental aspects of intergenerational relationships: reproduction and solidarity. Theoretically, family complexity is distinguished into four dimensions: (a) the length, timing and nature of exposure to the child, (c) biological relatedness to the child, and (c) characteristics of parent-parent ties (triadic effects), (d) characteristics of the wider family network. Using insights from several disciplines, I develop a common theoretical framework for understanding intergenerational reproduction and solidarity. To test the theory, an innovative multiactor survey is developed with an oversampling strategy in which for each adult child, information is collected on all parent figures, and for each parent, information on all adult children. In addition, register data are used to analyze one aspect of reproduction in a dynamic fashion (educational reproduction) and vignette data are used to analyze one aspect of solidarity in more depth (norms prescribing solidarity). By studying reproduction and solidarity as outcomes, I shift the traditional focus from examining how the SDT has affected individual well-being, to the question of how the SDT has affected relationships. In doing so, I analyze a new problem in demography and sociology and contribute to classic debates about population ageing and social inequality. Theoretically, the study of family complexity yields unique opportunities to test ideas about the nature of intergenerational relationships and will shed new light on the traditional dichotomy of social vis-à-vis biological bases of intergenerational relationships. Methodological innovation is made by developing solutions for well-known problems of multiactor data, thereby strengthening the theoretical relevance of survey data for the social sciences.

MAKSWELL project proposes to extend and harmonising the indicators able to capture the main characteristics of the beyond-GDP approach proposing a new framework that includes them in the evaluation of the public policies. The main goals of the project can be summarized in three main objectives. In particular, the project aims at: 1_building up a database for a wide set of EU countries that select and harmonize the national framework on well-being as well as the available SDG indicators; 2_improving the Database both in relation to the timeliness and to the integration with big data measures and the methodologies able to reach these extensions. Particularly WP2 will extend the actual set of information available on well-being and sustainability to including coherent new data sources (eg big data) able to derive coherent indicators for local analysis and disaggregation for other domain; 3_using the extended Database for policy evaluation and built up a national pilot studies. MAKSWELL project aims at creating a shared knowledge on the state of the art on relevant dimensions of sustainable development and on vulnerabilities and potentialities of society; on the most appropriate traditional and new data collection tools and modern statistical methods to have timely and accurate data. The analysis and summary of the main features of the piloting process will produce recommendations. They will be substantially suggestions and best practices collections to maintain and update the prototype fed by these new and traditional data sources for more focused policy decisions. The knowledge will be built up through the contact and reciprocal fertilization of NSIs, academics and stakeholders, identifying and contacting key practitioners to participate in a virtual Working Group. projects results will also path ways for common research under FP9.

NeEDS responds to the massive scientific and technological challenges that the very rapidly growing field of Data Science has created for users and producers of data in Europe and world-wide. The challenges stem from the complexity of the data, the completely novel questions posed to data scientists, as well as the need of non-experts to visualize and interact with the knowledge extracted from data in order to aid data-driven decision-making. Companies and public sector bodies around Europe find they cannot build up the required capabilities quickly enough, and Europe is remarkably behind US academia in increasing Data Science capacity. NeEDS provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics. NeEDS consists of four academic beneficiaries, eight industrial beneficiaries (from industry sectors ranging from energy, retailing, insurance to banking, as well as national statistical offices), two academic partners and one industrial partner from five EU countries, USA and Latin America with strong and complementary expertise. With this composition, NeEDS is in a unique position to deliver cutting-edge multidisciplinary research to advance academic thinking on Data Science in Europe, and to improve the Data Science capabilities of industry and the public sector.