A PANIC Attack on Unit Roots and Cointegration

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC—Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable‐specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid pooling of individual statistics and thus panel tests can be constructed. A distinctive feature of PANIC is that it tests the unobserved components of the data instead of the observed series. The key to PANIC is consistent estimation of the space spanned by the unobserved common factors and the idiosyncratic errors without knowing a priori whether these are stationary or integrated processes. We provide a rigorous theory for estimation and inference and show that the tests have good finite sample properties.     

Determining the Number of Factors in Approximate Factor Models

In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross‐sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.     

Just for fun? Alcohol and drugs in youth lifestyles: a survey

The article presents an analysis of the relationship between young people and alcohol and drug use, examining both their attitudes and behaviors. The analysis is based on statistics collected through a structured-questionnaire survey of 1071 students from the secondary schools of Rome and the Province. The first part of the article examines patterns and levels of alcohol and drug use and typical places of use. The central part is dedicated to the perceptions and opinions on alcohol and drugs, with particular regard to the dimensions of risk and sociality, and to the different roles of school and family in raising risk awareness. Sociality appears as a crucial element in young people's tendency to associate alcohol and drugs with an effect of relaxation and disinhibition that makes social relations easier. The article finally examines the relationship between young people and social norms, identifying transgressive models and habits. On the whole, the study highlights a connection between alcohol and drug use and desire of sociality, which is experienced more as a situational and ever-changing practice than as a search for close friendships.     

Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error

Small area estimators in linear models are typically expressed as a convex combination of direct estimators and synthetic estimators from a suitable model. When auxiliary information used in the model is measured with error, a new estimator, accounting for the measurement error in the covariates, has been proposed in the literature. Recently, for area‐level model, Ybarra & Lohr (Biometrika, 95, 2008, 919) suggested a suitable modification to the estimates of small area means based on Fay & Herriot (J. Am. Stat. Assoc., 74, 1979, 269) model where some of the covariates are measured with error. They used a frequentist approach based on the method of moments. Adopting a Bayesian approach, we propose to rewrite the measurement error model as a hierarchical model; we use improper non‐informative priors on the model parameters and show, under a mild condition, that the joint posterior distribution is proper and the marginal posterior distributions of the model parameters have finite variances. We conduct a simulation study exploring different scenarios. The Bayesian predictors we propose show smaller empirical mean squared errors than the frequentist predictors of Ybarra & Lohr (Biometrika, 95, 2008, 919), and they seem also to be more stable in terms of variability and bias. We apply the proposed methodology to two real examples.     

Siblings in lockdown: International evidence for birth order effects on child adjustment in the Covid19 pandemic

Supportive family relationships may mitigate the impact of the Covid19 pandemic on young children's adjustment, but existing work is limited by its focus on within-country variation and parental influences. Addressing these gaps, and drawing on reported buffering effects of older siblings on child mental health (Lawson and Mace, 2010), the current international study examined whether child adjustment problems were, on average, elevated by the pandemic and whether this buffering effect of older siblings would be maintained. In the first wave of the Covid19 pandemic (April to July 2020), 2516 parents of 3- to 8-year-old children living in Australia, China, Italy, Sweden, United Kingdom, and United States of America—six countries with contrasting governmental responses to the pandemic—completed an online survey about family experiences and relationships and child adjustment, as indexed by ratings on the Strengths and Difficulties Questionnaire (SDQ: R. Goodman, 1997). As expected, child SDQ total difficulty scores were elevated in all sites except Sweden (which notably did not enforce mass school closures). Compared to children without siblings, children with one or more older siblings showed fewer adjustment problems. Children from lone-parent households displayed more adjustment problems, as did those whose parents reported increased sibling conflict. Finally, child adjustment problems were negatively associated with family socio-economic status, but positively related to the indices of Covid-19 family disruption and government stringency. We discuss these findings in relation to existing work on asymmetric effects of older versus younger siblings, and siblings as sources of support.     

Children's trust in social workers: Scale development and relations to Children's engagement with social workers

This research aimed to develop the children's trust in general social workers (CTGSW) scale. Psychometric properties, structural validity, construct, and concurrent validity of the scale were evaluated. Both linear and quadratic patterns between children's trust beliefs in social workers and their engagement with social workers were examined. A sample of 112 Italian vulnerable children (M = 11.4 years, SD = 1 month) were administered the Italian‐Children's Generalized Trust Beliefs scale, the CTGSW scale, and a measure of engagement with social workers. The CTGSW scale demonstrated the expected (a) structure validity; (b) acceptable psychometric properties; (c) construct validity by correlations with trust in significant others; and (d) concurrent validity by associations with children's engagement with social workers. Reliability and honesty bases of trust in social workers were associated with engagement with social workers. In comparison to the middle range, children who held very low trust in social workers demonstrated very low quality of relation with social workers. The pattern was asymmetrical. Children who held high trust beliefs in social workers demonstrated a modest decrease in quality of relation with social workers. The findings demonstrated validity and utility of the CTGSW and yielded support for the basis, domain, and target framework.     

Italian Social Enterprises at the Crossroads: Their Role in the Evolution of the Welfare State

This paper investigates the relationship between the emergence of social enterprises (SEs) and the historical development of the Italian welfare state. Our research offers a comprehensive overview of the internal and external influences that shaped the constitutive relations between the welfare state and SEs. A qualitative methodology based on semi-structured interviews and focus groups has been adopted. This study suggests that two interconnected dynamics—the emergence of new social needs being answered by private organisations and the increased prominence of third sector actors during the privatisation of the welfare state—shaped the co-evolution of the welfare state and SEs in Italy. The study also suggests that the emergence and evolution of Social Enterprises in the years leading up to 2001 was mainly a bottom-up phenomena stemming from the actions of citizens setting up private organisations (often cooperatives) to answer to social problems created by new social needs and the structural reform of the welfare state. After 2001 especially with the new law on SEs in 2016, the evolution of SEs seems to have been increasingly influenced by the surrounding ecosystem of actors and supranational policy discourses rather than SEs themselves.     

Dynamic Identification of Dynamic Stochastic General Equilibrium Models

This paper studies dynamic identification of parameters of a dynamic stochastic general equilibrium model from the first and second moments of the data. Classical results for dynamic simultaneous equations do not apply because the state space solution of the model does not constitute a standard reduced form. Full rank of the Jacobian matrix of derivatives of the solution parameters with respect to the parameters of interest is necessary but not sufficient for identification. We use restrictions implied by observational equivalence to obtain two sets of rank and order conditions: one for stochastically singular models and another for nonsingular models. Measurement errors, mean, long‐run, and a priori restrictions can be accommodated. An example is considered to illustrate the results.