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The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Today’s data are increasingly complex and classical statistical techniques need growingly more refined mathematical tools to be able to model and investigate them. Paradigmatic situations are represented by data which need to be considered up to some kind of trans- formation and all those circumstances in which the analyst finds himself in the need of defining a general concept of shape. Topological Data Analysis (TDA) is a field which is fundamentally contributing to such challenges by extracting topological information from data with a plethora of interpretable and computationally accessible pipelines. We con- tribute to this field by developing a series of novel tools, techniques and applications to work with a particular topological summary called merge tree. To analyze sets of merge trees we introduce a novel metric structure along with an algorithm to compute it, define a framework to compare different functions defined on merge trees and investigate the metric space obtained with the aforementioned metric. Different geometric and topolog- ical properties of the space of merge trees are established, with the aim of obtaining a deeper understanding of such trees. To showcase the effectiveness of the proposed metric, we develop an application in the field of Functional Data Analysis, working with functions up to homeomorphic reparametrization, and in the field of radiomics, where each patient is represented via a clustering dendrogram.

The tipsae package implements a set of small area estimation tools for mapping proportions and indicators defined on the unit interval. It provides for small area models defined at area level, including the classical Beta regression, Zero and/or One Inflated Beta and Flexible Beta ones, possibly accounting for spatial and/or temporal dependency structures. The models, developed within a Bayesian framework, are estimated through Stan language, allowing fast estimation and customized parallel computing. The additional features of the tipsae package, such as diagnostics, visualization and exporting functions as well as variance smoothing and benchmarking functions, improve the user experience through the entire process of estimation, validation and outcome presentation. A Shiny application with a user-friendly interface further eases the implementation of Bayesian models for small area analysis.

The DMP is a document that provides details regarding all the research data collected and generated within a project. In particular, it explains the way research data are handled, organized, licensed and made openly available to the public, and how they will be preserved after the project is completed. The DMP also provides motivations when versions or parts of the project research data cannot be openly shared on account of third-party copyright issues, confidentiality, or personal data protection requirements, or when open dissemination could jeopardize the project achievements. The details and the final number of the project data sets may vary during the course of research. For this reason, it is fundamental to keep the DMP constantly updated.

The Italian region Emilia-Romagna ranks first among the world’s most important cooperative districts. Using a unique dataset covering all firms registered in the region, we investigate the performance of active firms in the period 2010–18. By focusing on added value, employment and profits of cooperative firms as compared to conventional firms, we disentangle the differences between the average performance of the two types of companies and detect the presence of a “size effect” driving much of the difference between them. Moreover, our results strengthen previous empirical evidence about the behavior of cooperative firms: they seem to optimize a mixture of employment and profits, assigning a greater weight to the former during downturns and stagnation. Hence, as a type of firm, they look more resilient than conventional companies, at least as far as employment is concerned. Finally, we examine the regional logistics industry and compare also the productivity per employee in the two segments of the sector.

The thesis deals with the problem of Model Selection (MS) motivated by information and prediction theory, focusing on parametric time series (TS) models. The main contribution of the thesis is the extension to the multivariate case of the Misspecification-Resistant Information Criterion (MRIC), a criterion introduced recently that solves Akaike’s original research problem posed 50 years ago, which led to the definition of the AIC. The importance of MS is witnessed by the huge amount of literature devoted to it and published in scientific journals of many different disciplines. Despite such a widespread treatment, the contributions that adopt a mathematically rigorous approach are not so numerous and one of the aims of this project is to review and assess them. Chapter 2 discusses methodological aspects of MS from information theory. Information criteria (IC) for the i.i.d. setting are surveyed along with their asymptotic properties; and the cases of small samples, misspecification, further estimators. Chapter 3 surveys criteria for TS. IC and prediction criteria are considered for: univariate models (AR, ARMA) in the time and frequency domain, parametric multivariate (VARMA, VAR); nonparametric nonlinear (NAR); and high-dimensional models. The MRIC answers Akaike’s original question on efficient criteria, for possibly-misspecified (PM) univariate TS models in multi-step prediction with high-dimensional data and nonlinear models. Chapter 4 extends the MRIC to PM multivariate TS models for multi-step prediction introducing the Vectorial MRIC (VMRIC). We show that the VMRIC is asymptotically efficient by proving the decomposition of the MSPE matrix and the consistency of its Method-of-Moments Estimator (MoME), for Least Squares multi-step prediction with univariate regressor. Chapter 5 extends the VMRIC to the general multiple regressor case, by showing that the MSPE matrix decomposition holds, obtaining consistency for its MoME, and proving its efficiency. The chapter concludes with a digression on the conditions for PM VARX models.

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

This thesis concerns the theory and the inference of a new class of independence models based on a graphical representation that we name profile graphs. Multiple graph models are special cases in this class and the compatibility in terms of independence structure is derived with respect to chain graph models of different types. Inference and model selection based on both Lasso methodology and Bayesian theory are studied and implemented. The thesis is composed of four chapters. In the first chapter, we present a literature review of multiple and chain graphs. Markov properties, parameterization and inference are reviewed for undirected, bidirected, LWF chain and regression graphs. In the second chapter, a class of profile graphs is introduced for modelling the effect of an external factor on the independence structure of a multivariate set of variables. Conditional and marginal independence structures are explored by using profile undirected and bi-directed graphical models, respectively. These two families of graphical models are formally defined with their corresponding Markov properties. Furthermore, necessary conditions are derived to induce, for any profile undirected and bi-directed graph model, a compatible class of chain graph models of different type known as LWF chain graph and regression graph, respectively. In the third chapter, we propose two Bayesian approaches for the selection of Ising models associated to multiple undirected graphs. We devise a Bayesian exact-likelihood inference for low-dimensional binary response data, based on conjugate priors for log- linear parameters. We also propose a quasi-likelihood Bayesian approach for fitting high-dimensional multiple Ising graphs, where the normalization constant results computationally intractable. In both methods, we define a Markov Random Field prior on the graph structures, which encourages the selection of the same edges in related graphs. Finally, in the fourth chapter we present some final remarks on Chapters 2 and 3.

In this thesis, we deal with the design of experiments in the drug development process, focusing on the design of clinical trials for treatment comparisons (Part I) and the design of preclinical laboratory experiments for proteins development and manufacturing (Part II). In Part I we propose a multi-purpose design methodology for sequential clinical trials. We derived optimal allocations of patients to treatments for testing the efficacy of several experimental groups by also taking into account ethical considerations. We first consider exponential responses for survival trials and we then present a unified framework for heteroscedastic experimental groups that encompasses the general ANOVA set-up. The very good performance of the suggested optimal allocations, in terms of both inferential and ethical characteristics, are illustrated analytically and through several numerical examples, also performing comparisons with other designs proposed in the literature. Part II concerns the planning of experiments for processes composed of multiple steps in the context of preclinical drug development and manufacturing. Following the Quality by Design paradigm, the objective of the multi-step design strategy is the definition of the manufacturing design space of the whole process and, as we consider the interactions among the subsequent steps, our proposal ensures the quality and the safety of the final product, by enabling more flexibility and process robustness in the manufacturing.

Our main objective is exploring the association between widespread prosperity and the presence of the cooperative movement at the regional level in Italy between 2010 and 2019. We summarize the widespread prosperity through an index originally proposed by Sen (1976) and we then perform a panel regression showing that there is a positive and significant association between such an index and the presence of the cooperative movement as captured by the relative size of cooperative employees. We also detect that the cooperative movement contributes to the regional prosperity more through its employment than in terms of the added value it generates. Moreover, the size of the cooperative presence significantly concurs to explain some large differentials among Italian regions.

The contribution of this thesis consists in proving that score driven models possess a novel, intuitive, high dimensional and global optimality criterion, called Conditional Expected Variation optimality that formalizes the following words from Creal et al. (2013) "The use of the score is intuitive. It defines a steepest ascent direction for improving the model's local fit in terms of the likelihood or density at time t given the current position of the parameter. This provides the natural direction for updating the parameter. " Indeed, the fact that the score defines a steepest ascent direction is crucial in deriving the results and for the proposed optimality criterion to hold. To prove the aforementioned property, a point of contact between the econometric literature and the time varying optimization literature will be established. As a matter of fact, the Conditional Expected Variation optimality can be naturally viewed as a generalization of the monotonicity property of the gradient descent scheme. A number of implications on the specification of score driven models are analyzed and discussed, even in the case of model misspecification.

This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a Gaussian stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density, and encompasses the traditional and inverse autocovariance functions. Its nonparametric estimator is based on the inverse discrete Fourier transform of the same power transformation of the pooled periodogram. The general result is then applied to the class of Gaussian stationary ARMA processes and its implications are discussed. We illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule-Walker system of equations in the generalised autocovariance estimator. Selection of the pooling parameter, which characterizes the nonparametric estimator of the generalised autocovariance, controlling its resolution, is addressed by using a multiplicative periodogram bootstrap to estimate the finite-sample distribution of the estimator. A multivariate extension of recently introduced spectral models for univariate time series is considered, and an algorithm for the coefficients of a power transformation of matrix polynomials is derived, which allows to obtain the Wold coefficients from the matrix coefficients characterizing the generalised matrix cepstral models. This algorithm also allows the definition of the matrix variance profile, providing important quantities for vector time series analysis. A nonparametric estimator based on a transformation of the smoothed periodogram is proposed for estimation of the matrix variance profile.

Mixture models provide a useful tool to account for unobserved heterogeneity, and are the basis of many model-based clustering methods. In order to gain additional flexibility, some model parameters can be expressed as functions of concomitant covariates. In particular, prior probabilities of latent group membership can be linked to concomitant covariates through a multinomial logistic regression model, where each of these so-called component weights is associated with a linear predictor involving one or more of these variables. In this Thesis, this approach is extended by replacing the linear predictors with additive ones, where the contributions of some/all concomitant covariates can be represented by smooth functions. An estimation procedure within the Bayesian paradigm is proposed. In particular, a data augmentation scheme based on difference random utility models is exploited, and smoothness of the covariate effects is controlled by suitable choices for the prior distributions of the spline coefficients. This methodology is then extended to include flexible covariates effects also on the component densities. The performance of the proposed methodologies is investigated via simulation experiments and applications to real data. The content of the Thesis is organized as follows. In Chapter 1, a literature review about mixture models and mixture models with covariate effects is provided. After a brief introduction on Bayesian additive models with P-splines, the general specification for the proposed method is presented in Chapter 2, together with the associated Bayesian inference procedure. This approach is adapted to the specific case of categorical and continuous manifest variables in Chapter 3 and Chapter 4, respectively. In Chapter 5, the proposed methodology is extended to include flexible covariate effects also in the component densities. Finally, conclusions and remarks on the Thesis are collected in Chapter 6.

Statistical inference for discrete-valued time series has not been developed as systematically as traditional methods for time series generated by continuous random variables. This Ph.D. dissertation deals with time series models for discrete-valued processes. In particular, Chapter 2 is devoted to a comprehensive overview of the literature about observation-driven models for discrete-valued time series. Derivation of stochastic properties for these models is presented. For the inference, general properties of the quasi maximum likelihood estimator (QMLE) are discussed, followed by an illustrative application. In Chapter 3, a general class of observation-driven time series models for discrete-valued processes is introduced. Stationarity and ergodicity are derived under easy-to-check conditions, which can be directly applied to all the models encompassed in the framework. Consistency and asymptotic normality of the QMLE are established, with the focus on the exponential family. Finite sample properties of the estimators are investigated through a Monte Carlo study and illustrative examples are provided. The framework introduced in the paper provides a self-contained background that relates different models developed in the literature as well as novel specifications and makes them fully applicable in practice. Discrete responses are commonly encountered in real applications and are strongly connected to network data. The specification of suitable network autoregressive models for count time series is an important aspect which is not covered by the existing literature. In Chapter 4, we consider network autoregressive models for count data with a known neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The work is complemented by simulation results and a data example.

This document reports on activities carried out under Task 6.4 (Multiplier Events) in the framework of WP6 (Dissemination and outreach) of the CONSOLE project. This document includes information on the first round off Multiplier Events carried out by partners in the first few months of the project in order to attract attention and receive input from participants about the planned activities. In total there were 13 MEs held during the first few months of the project. A second round of Multiplier Events will take place at the end of the project.

Summary of main dimensions of the framework and of the contractual solutions identified in non-technical language.

The aim of task 2.3 is to collect the most promising and successful experiences outside the EU that could add new and interesting perspectives for application in EU and to feed into WP2 a wider range of opportunities for contract design. The task takes the form of a systematic literature review and this report provides a draft version of the outcomes of this review. The final version of the report will be provided in month 30. The review selected 79 documents, including both peer review papers and reports/grey literature having as a scope all countries outside those already represented by the CONSOLE partners. The main reasons for success identified for the reviewed cases are: a) reducing risks linked to results; b) reduced costs for monitoring results; c) farmers’ interest and social revenue; d) resources availability; e) additionality; f) relying on existing collectives; g) communication; payment setting; h)appropriate intermediaries. To a large extent the success factors listed above confirm insights from cases in the CONSOLE partner countries. However, they allow the consideration of a broader variety of solutions. On the other hand, this also depends on the specific institutional context where they are located, which means that potential for replication should be taken carefully. The fact that a large part of the experiences and certainly of the evidence is rather recent encourages the continuation of this task providing updates during the whole project life.

This document consists of Task 6.1 (Dissemination and communication strategy) in the framework of WP6 (Dissemination and outreach) and Deliverable 6.5 (Report on dissemination and communication strategy). The main aim will be to boost the impact of the project, through communicating the knowledge and results of the project. The project will target a wide range of audiences in order to link research, evidence-based policy and public awareness. Communication activities, such as workshops, social media accounts and websites have been discussed in this plan as well as identified actors and stakeholders in order to effectively promote innovation in delivery of agri-environmental climate public goods (AECPG) by EU agriculture and forestry. This is a living document and will be continuously updated according to CONSOLE’s progress.

Many CONSOLE partners are themselves involved in designing and testing of novel contract solutions at local level, either in the position as scientists, as key stakeholders and as representatives of practitioners who engage in contracts for the delivery of agri-environment- climate public goods (AECPGs). At case study level the focus lies on one specific contract solution for the delivery of one (or a limited number of) well defined AECPGs, while in CONSOLE the overarching objective is to co-construct an end-user led contractual framework and a practical contract solutions catalogue covering the four contract types result-based/ result- oriented, collective implementation/ cooperation, value chain and land tenure based as well as combinations or hybrids of them. The solution catalogue is foreseen to be usable by a wide range of end-users and actors and therefore needs to undergo a ground truth real-life testing to be organized by WP5. The CONSOLE Community of Practice (CoP) with its local, regional and/or national subgroups has therefore been set up. In order to enable a harmonized approach across all CONSOLE partner countries these guidelines provide a roadmap for the engagement with the CoP members throughout the project with particular emphasis on certain phases where a specific feedback or input for the task fulfilment is required. CoP members are core actors for the iterative process designated for the co-development of the contractual framework and solutions catalogue. Thanks to their involvement they can on the one hand directly benefit from insights gained from the diagnostics of existing experiences with AECPGs (WP2), from feasibility assessments (WP3), and from simulations and performance modelling (WP4). On the other hand, members of the CoP may themselves participate in the WPs 2, 3 and 4 in their countries and can here contribute to practicable solutions, besides through their involvement in dedicated feedback rounds. This document provides guidelines for partners for testing the framework and solutions catalogue, including indications about approach, planning and reporting. It outlines the organization of the feedback process to be organized in a harmonized way across CONSOLE partners and how to take advantage of ongoing case study activities at national level. Furthermore, it provides guidance on the tasks to be carried out by the national focal points. The contact persons of the focal points play a key role to facilitate the CoP process at national level, to report outcomes of feedback loops and to keep track of the actors involved in the CoP. Finally, first insights into planned training activities targeted to practitioners involved in the CoP back-to-back with the framework testing will be provided. It is important that training is of mutual benefit – for CoP members and in particular farmers, but also a way of gathering valuable insights for CONSOLE work itself. With the help of the CoP, the CONSOLE framework will develop into a supporting tool for actors in the field, enabling a better delivery of AECPGs by agriculture and forestry.

This document represents deliverable D2.4 “Report on WP2 lessons learned” within work package WP2 “Diagnostic of existing experiences on AECPGs” of the EU Horizon 2020 project CONSOLE. The document brings together results and conclusions of WP2 Task 2.5. Particularly, the results from the analysis of solutions from inside the EU will be complemented with experiences beyond the CONSOLE EU case studies in order to derive inspiration and additional ideas for improvements of European solutions in WP3 and WP4. Moreover, the lessons learned will give first an overview for policy makers on innovative and successful solutions “outside the box”.

To improve the provision of agri-environmental-climate public goods (AECPGs) from agriculture and forestry, new solutions, such as result-based payments or collective implementation, and new strategies along the value chain might help. The analysis of ca. 120 case studies in and outside EU showed that many real-life cases of implementation combine different contract types. Collective implementation can be a precondition for environmental effectiveness, particularly if AECPGs are addressed that can be hardly improved by measures on single plots, (e.g. water quality). Result-based contracts can target specific AECPGs and increase engagement of land-managers due to flexible management choices. Some recommendations for contract design and implementation are distilled: 1.) Targeting contracts to specific regions addresses regional criticalities and enhances the farmers’ and foresters’ interest and understanding of measures. 2.) Involving land-managers in target-setting and measure development leads to higher compatibility with their businesses and can create win-win situations. 3.) Involving control authorities in the design of indicators in result-based schemes can guarantee integrability into RDPs. 4.) Fostering bottom-up approaches and involving regional key actors as coordinating units enhances commitment and motivation in collective approaches. 5.) Guaranteeing good levels of equity and fairness enhances acceptance particularly in value-chain based solutions. It becomes clear that result-based and collective solutions don’t fit in each context situation, as they often demand high levels of knowledge and collaborative skills. Value chain approaches are often only suited if consumers’ awareness is high.

As societies age, the well-being of the elderly increasingly becomes a priority and a challenge. Measuring the quality of life of older people and identifying its determinants is a fundamental element, that could help in designing tailored policies for making aged people well-being increase all over Europe. Most of research on active ageing stresses the importance of individual determinants of wellbeing, and some scholars suggest that macro factors have also a role, for instance the welfare provisions and models, as well as the level of socio-economic inequality, unemployment rate, gender equality policies, GDP. However, in the quoted studies only single macro dimensions are generally considered, while a research linking the multiple macro-dimensions of active aging to the individual level of wellbeing is lacking. We want to fill this gap, aware that both active ageing (as a macro-level element) and individual well-being are complex concepts, influenced and determined by several aspects, and that research results could be influenced by the type of dimension chosen for the analysis. Beyond understanding the micro-level elements influencing individual well being, we want to “put individuals into contexts”, and to investigate the role macro- level factors have in explaining individual wellbeing. The novelty element of our research is that we use composite measures, both at the micro and macro-level. We focus on a broad definition of quality of life in old age, capturing the multidimensional nature of such a concept by using the CASP-12 indicator drawn from the sixth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE). We chose as a macro-level composite indicator the Active Ageing Index (AAI), that depict an overall picture synthetizing several aspect considered to be important gauges of active ageing at the national level. We use multilevel models, by taking into account the AAI measure as a level-2 variable, both in its elementary components and as a whole, to in depth investigate which macro-level factors foster/hinder elderlies’ life quality. Result seems to corroborate the hypothesis that the context matters and cannot be ignored in the analysis. This confirm other results found in the literature on the importance of the macro factors on individual’s wellbeing and support the idea that using macro indicators in multilevel analysis increase the explaining potential of the study.

According to recent literature the increasing women’s labour market participation is only the first part of the so called gender revolution, while a second part is now unfolding, with an increased participation of men in family life with special attention to childcare. The aim of this paper is to explore fathers’ involvement in parenting tasks within different contexts in terms of gender regimes, family policies, and workplace culture. The idea is to evidence individual factors that may enable/challenge the capability of fathers to stay with children and care for them, and to suggest opportune father-friendly policies. Time with children is compared among a sample of fathers in Time Use survey in France (2009-2010), Italy (2008-09), Sweden (2000-2001) and the UK (2000). Three different measures of father involvement are examined: the total time father spend with their children, the time they spend alone with them, and their engagement in childcare activities. Results show that distinct micro-level factors contribute in determining the three levels of father’s commitment analysed. Few cross-countries differences emerge. Fathers’ involvement is mainly determined by their work-related features, by their children characteristics, and by their partner’s working schedules. Weekday and weekend differences are observed. The quantum of father engagement strongly depends on the countries’ institutional context: it is the highest in Sweden and the lowest in Italy. Fathers’ time with children at the crossroads of the gender revolution: 3 a comparative analysis in France, Italy, Sweden and the UK Acknowledgement: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 320116 for the research project FamiliesAndSocieties. This comparative study shows the methodological importance of considering different measures of father involvement to understand how micro-level factors influence the time fathers spend with their children in different institutional context.

The aim of this paper is to study the role of education as a micro-level determinant of childlessness in Finland and Italy paying attention to the role of union formation. We start from the hypothesis that when modeling the relation between childlessness and education, selectivity processes have to be considered. Our hypothesis is that education can affect childlessness, both directly, and indirectly, through its link with the union formation mechanisms; and that decisions on union formation and on parenthood can be considered as jointly determined. Thus, in order to understand how education can influence childlessness, it is important take into account the possible existence of common, unobserved factors that determine both processes simultaneously. We use Bivariate Probit Models to simultaneously model both union formation and childlessness mechanisms, and to understand which is the role education play in determining such mechanisms. We focus on Italy and Finland, which are characterized by very different institutional contexts, being at different development stages, but showing outstanding level of childlessness with a prevalence over 20% for the cohorts born in the sixties. The similar prevalence observed for the most recent cohort hides interesting differences in terms of childlessness path by education level: Italy shows a persistent positive educational gradient over birth cohort, while in Finland it has reversed, turning out to be negative among the most recent birth cohorts. In a comparative perspective, we are interested in understanding if the cultural and political-institutional context of countries can determine the way childlessness and education are related. We use data from the Finnish Late Fertility Survey 2015 and from the 2009 Multipurpose Italian survey, Family and Social Subjects. Results confirm that a positive education gradient in childlessness exists in Italy, even when taking into account the correlation between the preferences of women with respect to union formation and motherhood processes; while in Finland the negative education gradient reverses when controlling for this correlation as partnership market dynamics strongly influence the educational gradient in childlessness, whose existence is mostly due the difficulty low educated women encounter in finding a partner.

The concept of actively ageing is strictly related to the notion of dealing with time: finding a new role in society means to replace structured time use routines with new ones, giving old people the chance to establish and discover a renewed meaning of life. Research has shown that being active is an important determinant of LS. Moreover, not only the type of activity seems to be important, but also its social dimension. In this study we use Time Use survey for understanding if and to what extent involvement in different activities – measured through time devoted to each activity – is associated with different levels of LS. Moreover, we want to know if performing those activities alone or with other people is significantly associated to LS level, with the hypothesis that impairment in social interactions and isolation are important sources of dissatisfaction in old age, while supportive social relationships and intimacy may increase emotional strength and LS. We select a subsample of 12,247 individuals, aged more than 60 from the 2015-2016 Italian Time Use Survey. We use OLS regression models for studying the association between self-reported LS (10 point Likert scale) and the time spent in passive, active, and social pursuits. By starting from the hypothesis that different aspects are likely to be important in the evaluation of self, and, in turn, in determining wellbeing, for men and women, we run separate analyses by gender. Moreover by considering that being active, and, in particular, being active in social activities and spending time not alone may have a more relevant role in determining LS when the older adult is in a condition of social frailty, such as that defined by living alone, we split our analyses by living arrangements (alone vs. with others). We can expect, in fact, that the potential gender differences in the predictors of LS vary for older persons living alone, as they have specific characteristics and needs, and, even if they are not necessarily socially isolated, their condition places them in a potential vulnerable position. Results show that being active is important for LS in old age. Spending time in social activities resulted to be associated with LS, regardless the sex. At the same time, the proportion of daily activities spent with other people is relevant only for people not living alone.

Given the progressive ageing of Italian and European populations, chronic diseases attributable to ageing are rising steeply, calling for new strategies for health resources management and implementation of prevention policies. Among chronic patients, frail subjects have special and wider care needs, together with an increased risk of adverse health outcomes. Thus, their identification is a fundamental goal, claimed as the first step of the Italian National Program for Chronic Diseases.