Individual determinants of social behavior

By using unique data from the section on social behavior of the Bank of Italy's 2004 Survey of Household Income and Wealth (SHIW), the paper studies the individual determinants of several aspects of social behavior: attitudes to cooperating with anonymous others; interest in politics; participation in groups and associations; and propensity to rely on favoritism both in finding a job and in dealing with government red tape. Our findings suggest that these different aspects of social behavior are only weakly correlated to each other and are explained by different individual determinants. We find that older and more educated individuals display a greater willingness to cooperate, a stronger interest in politics, and more intense association activity. By contrast, the likelihood of relying on favoritism does not depend on age and education. We also find that home-ownership is associated with good social conduct, while urban residence has mostly a negative impact on public behavior. Finally, having left-wing political opinions increases the interest in politics, while it does not affect the other aspects of social behavior.     

Formative Indicators and Effects of a Causal Model for Household Human Capital with Application

Dagum and Slottje (2000) estimated household human capital (HC) as a latent variable (LV) and proposed its monetary estimation by means of an actuarial approach. This paper introduces an improved method for the estimation of household HC as an LV by means of formative and reflective indicators in agreement with the accepted economic definition of HC. The monetary value of HC is used in a recursive causal model to obtain short- and long-term multipliers that measure the direct and total effects of the variables that determine household HC. The new method is applied to estimate US household HC for year 2004.     

Estimates based on preliminary data from a specific subsample and from respondents not included in the subsample

Various approaches to obtaining estimates based on preliminary data are outlined. A case is then considered which frequently arises when selecting a subsample of units, the information for which is collected within a deadline that allows preliminary estimates to be produced. At the moment when these estimates have to be produced it often occurs that, although the collection of data on subsample units is still not complete, information is available on a set of units which does not belong to the sample selected for the production of the preliminary estimates. An estimation method is proposed which allows all the data available on a given date to be used to the full-and the expression of the expectation and variance are derived. The proposal is based on two-phase sampling theory and on the hypothesis that the response mechanism is the result of random processes whose parameters can be suitably estimated. An empirical analysis of the performance of the estimator on the Italian Survey on building permits concludes the work. The Sects. 1,2,3,4 and the technical appendixes have been developed by Giorgio Alleva and Piero Demetrio Falorsi; Sect. 5 has been done by Fabio Bacchini and Roberto Iannaccone. Piero Demetrio Falorsi is chief statisticians at Italian National Institute of Statistics (ISTAT); Giorgio Alleva is Professor of Statistics at University “La Sapienza” of Rome, Fabio Bacchini and Roberto Iannaccone are researchers at ISTAT.     

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.