Assessing Parfit’s Repugnant Conclusion within a canonical endogenous growth set-up

Parfit’s Repugnant Conclusion stipulates that under total utilitarianism, it might be optimal to choose increasing population size while consumption per capita goes to zero. We evaluate this claim within a canonical AK model with endogenous population size and a reduced form relationship between demographic and economic growth. First we characterize the optimal solution paths for any capital dilution function. Second, we prove that while the Repugnant Conclusion can never occur for realistic values of intertemporal substitution in the traditional linear dilution model, it does occur when population growth is linked to economic growth via an inverted U-shaped relationship.     

Self-selection and direct estimation of across-regime correlation parameter

A direct maximum likelihood (ML) procedure to estimate the ‘generally unidentified’ across-regime correlation parameter in a two-regime endogenous switching model is here provided. The results of a Monte Carlo experiment confirm consistency of our direct ML procedure, and its relative efficiency over widely applied models and methods. As an empirical application, we estimate a two-regime simultaneous equation model of domestic work of Italian married women in which the two regimes are given by their working status (employed or unemployed).     

Learning the Wealth of Nations

We study the evolution of market‐oriented policies over time and across countries. We consider a model in which own and neighbors' past experiences influence policy choices through their effect on policymakers' beliefs. We estimate the model using a large panel of countries and find that it fits a large fraction of the policy choices observed in the postwar data, including the slow adoption of liberal policies. Our model also predicts that there would be reversals to state intervention if nowadays the world was hit by a shock of the size of the Great Depression.     

Discussing Measures of Financial Inclusion for the Main Euro Area Countries

Since financial inclusion has become a policy target in many countries, properly measuring it is crucial. Usual indexes of financial inclusion include inappropriate variables and don’t take into account other relevant aspects, thus misrepresenting the phenomenon. In this work we focus on the diffusion of electronic cards, generally not included in the usual indexes of financial inclusion notwithstanding they provide alternatives to usual saving practices and allow less costly transactions across larger markets and wider geographic areas. We show that, taking these instruments into account, the comparative valuation of the degree of financial inclusion between the main euro area countries changes substantially. We also employ survey data to analyze cross-country differences in the degree of financial inclusion and the distribution of multidimensional deprivations of specific sub-groups of populations.

     

Econometrics exams and round numbers: Use or misuse of indirect estimation methods?

The use of Monte Carlo methods to generate exam datasets is nowadays a well-established practice among econometrics and statistics examiners all over the world. Its advantages are well known: providing each student a different data set ensures that estimates are actually computed individually, rather than copied from someone sitting nearby. The method however has a major fault: initial “random errors,” such as mistakes in downloading the assigned dataset, might generate downward bias in student evaluation. We propose a set of calibration algorithms, typical of indirect estimation methods, that solve the issue of initial “random errors” and reduce evaluation bias. Ensuring round initial estimates of the parameters for each individual dataset, our calibration procedures allow the students to determine if they have started the exam correctly. When initial estimates are not round numbers, this random error in the initial stage of the exam can be corrected for immediately, thus reducing evaluation bias. The procedure offers the further advantage of rounding markers’ life by allowing them to check round numbers answers only, rather than lists of numbers with many decimal digits¹.     

Conversations for Connection: An Outcome Assessment of the Hold‐Me‐Tight Relationship‐Education Program,and Recommendations for Improving Future Research Methodology in Relationship Education

The Hold Me Tight (HMT) program is a new approach to relationship education based on Emotionally‐Focused Therapy (Johnson, 2004), an evidence‐based approach to couple therapy. In this exploratory longitudinal research, we examined individual growth in relationship satisfaction and trust for partners in 95 couples in 16 HMT groups across four occasions of measurement: Baseline, Pre‐Program, Post‐Program and at either 3‐ or 6‐month Follow‐Up. We found that relationship satisfaction and trust increased during program participation, and declined during follow‐up. We believe our findings provide support for the short‐term effectiveness of the HMT program, and suggest a longer period of program delivery may result in improved retention of gains. Finally, we present recommendations for improving the design of future longitudinal research in relationship education.     

Estimating multiple-membership logit models with mixed effects: indirect inference versus data cloning

Multiple-membership logit models with random effects are models for clustered binary data, where each statistical unit can belong to more than one group. The likelihood function of these models is analytically intractable. We propose two different approaches for parameter estimation: indirect inference and data cloning (DC). The former is a non-likelihood-based method which uses an auxiliary model to select reasonable estimates. We propose an auxiliary model with the same dimension of parameter space as the target model, which is particularly convenient to reach good estimates very fast. The latter method computes maximum likelihood estimates through the posterior distribution of an adequate Bayesian model, fitted to cloned data. We implement a DC algorithm specifically for multiple-membership models. A Monte Carlo experiment compares the two methods on simulated data. For further comparison, we also report Bayesian posterior mean and Integrated Nested Laplace Approximation hybrid DC estimates. Simulations show a negligible loss of efficiency for the indirect inference estimator, compensated by a relevant computational gain. The approaches are then illustrated with two real examples on matched paired data.     

Making visits better: the perspectives of parents,foster parents,and child welfare workers

Mothers of children recently placed in foster care, foster mothers, and child welfare workers participated in semistructured, clinical interviews focusing on the challenges of parent visitation with young children. Mothers described their feelings of grief, trauma, and rage about the forced separation from their children and stressed the importance of emotional expression and communication during visits. Child welfare workers described the complexities of supporting emotionally close parent-child interactions while monitoring and assessing parental behavior during visits. Foster mothers described the importance of preparing children for visits and the difficulties of supporting the children afterward. Implications of understanding mothers', foster mothers', and child welfare workers' perspectives on enhancing the quality of visits with young children are discussed.