Approximations to the Truth: Comparing Survey and Microsimulation Approaches to Measuring Income for Social Indicators

In this paper, we evaluate income distributions in four European countries (Austria, Italy, Spain and Hungary) using two complementary approaches: a standard approach based on reported incomes in survey data, and a microsimulation approach, where taxes and benefits are simulated. These two approaches may be expected to generate slightly different results, particularly in respect of individuals on lower incomes, because benefit receipts tend to be under-reported in survey data, and over-estimated in microsimulation procedures. However, we find that the two approaches do in fact produce reasonably consistent results, in terms of both inequality measures and poverty rates. To the extent that the results differ, we explore the reasons why these differences arise, and suggest directions for future research, in which each approach may inform improvements in the other.     

Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model

We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The proposed approach is illustrated by two empirical applications: the first is based on data coming from the Panel Study of Income Dynamics and concerns employment and fertility; the second is based on the Health and Retirement Study and concerns the self reported health status.     

Relabelling in Bayesian mixture models by pivotal units

Label switching is a well-known and fundamental problem in Bayesian estimation of finite mixture models. It arises when exploring complex posterior distributions by Markov Chain Monte Carlo (MCMC) algorithms, because the likelihood of the model is invariant to the relabelling of mixture components. If the MCMC sampler randomly switches labels, then it is unsuitable for exploring the posterior distributions for component-related parameters. In this paper, a new procedure based on the post-MCMC relabelling of the chains is proposed. The main idea of the method is to perform a clustering technique on the similarity matrix, obtained through the MCMC sample, whose elements are the probabilities that any two units in the observed sample are drawn from the same component. Although it cannot be generalized to any situation, it may be handy in many applications because of its simplicity and very low computational burden.     

Dynamic model-based clustering for spatio-temporal data

In many research fields, scientific questions are investigated by analyzing data collected over space and time, usually at fixed spatial locations and time steps and resulting in geo-referenced time series. In this context, it is of interest to identify potential partitions of the space and study their evolution over time. A finite space-time mixture model is proposed to identify level-based clusters in spatio-temporal data and study their temporal evolution along the time frame. We anticipate space-time dependence by introducing spatio-temporally varying mixing weights to allocate observations at nearby locations and consecutive time points with similar cluster’s membership probabilities. As a result, a clustering varying over time and space is accomplished. Conditionally on the cluster’s membership, a state-space model is deployed to describe the temporal evolution of the sites belonging to each group. Fully posterior inference is provided under a Bayesian framework through Monte Carlo Markov chain algorithms. Also, a strategy to select the suitable number of clusters based upon the posterior temporal patterns of the clusters is offered. We evaluate our approach through simulation experiments, and we illustrate using air quality data collected across Europe from 2001 to 2012, showing the benefit of borrowing strength of information across space and time.     

Book Review: Daniel Courgeau (ed.),Methodology and Epistemology of Multilevel Analysis. The Netherlands: Kluwer Academic Publishers, 2003, ix + 235 pp.

Book Review

Book Review: Daniel Courgeau (ed.), Methodology and Epistemology of Multilevel Analysis. The Netherlands: Kluwer Academic Publishers, 2003, ix + 235 pp.     

Pitfalls in linear models for style analysis

We discuss the statistical properties of return-based OLS style analysis introduced by Sharpe (1992). The aim of style analysis is to infer a fund managers investment decisions using only publicly available data on the fund performance and on the time evolution of market indexes. We show that the model proposed by Sharpe suffers of relevant drawbacks, most notably that it fails to yield correct results even in the simple case of a buy-and-hold strategy that only invests in the market indexes. Under this hypothesis we show that a model linear in index levels, as opposed to index returns, estimated via a Kalman filter avoids Sharpes model drawbacks. We further extend our analysis to strategies where the fund manager policy changes with time and the asset classes in which the fund manager invests are not known exactly. In this last case we show that a style analysis is possible only conditional to either an orthogonality hypothesis on the active investment strategy, or by the introduction of suitable instrumental variables.The authors are grateful to the editor and an anonymous referee for many comments which greatly helped in improving the paper. The authors are, obviously, fully responsible for any remaining error.     

A class of estimators for quantitative sensitive data

Randomized response methods for quantitative sensitive data are treated in an unified approach which includes the use of auxiliary information at the estimation stage. A class of estimators for the mean of a sensitive variable is proposed under a generic randomization model and the optimum estimator is obtained. Some special models are discussed in detail. To evaluate the degree of respondents’ confidentiality in models using auxiliary variables, a new measure of privacy protection is introduced. Different models are then compared both from the perspective of efficiency and privacy protection.     

Restarting “Normal” Life after Covid-19 and the Lockdown: Evidence from Spain,the United Kingdom,and Italy

Social Indicators Research - In this article, we examine the expectations of the economic outlook, fear of the future, and behavioural change during the first Covid-19 wave, for three European...     

Decompositions by sources and by subpopulations of the Pietra index: two applications to professional football teams in Italy

In this paper two innovative procedures for the decomposition of the Pietra index are proposed. The first one allows the decomposition by sources, while the second one provides the decomposition by subpopulations. As special case of the latter procedure, the “classical” decomposition in two components (within and between) can be easily obtained. A remarkable feature of both the proposed procedures is that they permit the assessment of the contribution to the Pietra index at the smallest possible level: each source for the first one and each subpopulation for the second one. To highlight the usefulness of these procedures, two applications are provided regarding Italian professional football (soccer) teams.