Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Government policy has placed increasing emphasis on the need for robust labour market projections. The job vacancy rate is a key indicator of the state of the economy underpinning most monetary policy decisions. However, its variation over time is rarely studied in relation to employment variations, especially at the sectoral level. The present paper assesses whether changes in the number of vacancies from quarter to quarter are a leading anticipator of employment variation in certain economic sectors over the previous decade in Italy, using multivariate time-series tools (the vector autoregressive and error correction models) with Eurostat data. As robustness checks for integration order and cointegration, we compare traditional critical values with those provided by response surface models. To the best of our knowledge, no previous study has evaluated this relationship using Italian data over the last decade. The results demonstrate that percentage changes in numbers employed (occupied persons) react to percentage changes in vacancies (one-quarter lagged), but not vice versa, indicating that variations of vacancies are weakly exogenous. The fastest short-term adjustment from disequilibrium is seen in the construction industry, whereas the manufacturing and the information and communication technology sectors demonstrate the strongest long-run relationships among variations. This suggests that the matching rates - the likelihood that a vacancy is filled - are higher for these than for other sectors, as a result of developments in recruitment technology for professional figures of such industries

The estimation of Human Capital in structural models with flexible specification

The present paper focuses on statistical models for estimating Human Capital (HC) at disaggregated level (worker, household, graduates). The more recent literature on HC as a latent variable states that HC can be reasonably considered a broader multi-dimensional non-observable construct, depending on several and interrelate causes, and indirectly measured by many observed indicators. In this perspective, latent variable models have been assuming a prominent role in the social science literature for the study of the interrelationships among phenomena. However, traditional estimation methods are prone to different limitations, as stringent distributional assumptions, improper solutions, and factor score indeterminacy for Covariance Structure Analysis and the lack of a global optimization procedure for the Partial Least Squares approach. To avoid these limitations, new approaches to structural equation modelling, based on Component Analysis, which estimates latent variables as exact linear combinations of observed variables minimizing a single criterion, were proposed in literature. However, these methods are limited to model particular types of relationship among sets of variables. In this paper, we propose a class of models in such a way that it enables to specify and fit a variety of relationships among latent variables and endogenous indicators. Specifically, we extend this new class of models to allow for covariate effects on the endogenous indicators. Finally, an application aimed to measure, in a realistic structural model, the causal impact of formal Human capital (HC), accumulated during Higher education, on the initial earnings for University of Milan (Italy) graduates is illustrated.

On the Relationships among Latent Variables and Residuals in PLS Path Modeling: the Formative-Reflective Scheme

A new approach for the estimation and the validation of a Structural Equation Model with a formative-reflective scheme is presented. The basis of the paper is a proposal for overcoming a potential deficiency of PLS Path Modeling. In the PLS approach the reflective scheme assumed for the endogenous latent variables is inverted; moreover, the model errors are not explicitly taken into account for the estimation of the endogenous latent variables. The proposed approach utilizes all the relevant information in the formative manifest variables providing solutions which respect the causal structure of the model. The estimation procedure is based on the optimization of the redundancy criterion. The new approach, entitled Redundancy Analysis approach to Path Modeling is compared with both traditional PLS Path Modeling and LISREL methodology, on the basis of real and simulated data.Latent Variables, Partial Least Squares, PLS Path Modeling, Redundancy Analysis, LISREL Model

La stima di variabili latenti da variabili osservate miste

The aim of this paper is to propose a general nonparametric model to estimate latent variables with scores non indeterminate; in this paper, following other approaches (PLS, RCD, RCDR), a latent variable (LV) is conceived as a linear combination of predictors (causes) which best predicts a set of dependent variables (indicators), maximising, in this manner, all available information about a LV in the soecified model. The model is also extented to categorical variables (nominal, ordinal) by means of optimal scaling methodology and applied to the estimate of a bidimensional LV as a proxy of human capital for US families in 1983

Proposta metodologica per lo studio delle relazioni spaziali tra unità microterritoriali con dati aggregati

The cartographic representation of spatial data is a useful tool to highlight of desease/excellence among small areas as regard to an indicator of interest; otherwise possible spatial correlations are not immediatly pointed out in the analysis. In this paper we propose a methodology to perform a ranking of small areas (SubT) for level (1 Y ) and temporary variations  (2 Y ) of a discretized numeric indicator y, starting from aggregate data (big areas, UT). Many methodologies which simultaneously want to estimate the scores of 1 Y , 2 Y and the factors which produce the ranking performed are based on principal components, but, in our opinions, other approachs seem more appropriate, for example, predictive methods based on (generalised) regression. Unfortunately this methodology is not applicable in the contest of cartographic representation of a discretized dependent variable and we propose an Ordinal Logit model for 1 Y  and 2 Y : the specified model allows to investigate the role (weight) of predictors in the determination of the ranking performed (on UT) and suggest a way of ranking the SubT

BALANCED SCORECARD HEALTH SYSTEM: A LATENT VARIABLE APPROACH

Abstract Th

Relative and impact evaluation of clinic and epidemiologic framework

The present paper addresses in detail the specification of a model that values the effectiveness of institutional plans within clinical or epidemiologist projects previewing the supply of a service to the person and jointly the effectiveness of agencies (Agents) that distribute the same service with different modalities, supplying the rationale for a rigorous procedure of objective evaluation. Finally the model proposed is applied to a public plan promoted from the Regione Lombardia in 2002, consisting in an economic contribution to family that decide to assist in house the not self-sufficient old, like alternative to the shelter in Residences

The estimate of clinical outcomes by Rasch analysis

This study describes and analyses the optimal property that the proposed methods of latent variables estimation should have to guarantee the availability of objective perform-ance measures in the case of evaluation of institutional initiatives and/or of a set of agents that offer a public service (Hospitals, Schools, etc). In particular the Rasch model will be assessed and subsequently it will be applied to the objective measurement of the cognitive status dependency regarding a sample of elderly patients involved in a plan of Regione Lombardia, named Buono Socio Sanitario, consist-ing in an economic contribution to the families that decide to attend the not self-sufficient elderly in family, as an alternative to the shelter in Residences

Benchmarking Strategies for Measuring the Quality of Healthcare: Problems and Prospects

Over the last few years, increasing attention has been directed toward the problems inherent to measuring the quality of healthcare and implementing benchmarking strategies. Besides offering accreditation and certification processes, recent approaches measure the performance of healthcare institutions in order to evaluate their effectiveness, defined as the capacity to provide treatment that modifies and improves the patient’s state of health. This paper, dealing with hospital effectiveness, focuses on research methods for effectiveness analyses within a strategy comparing different healthcare institutions. The paper, after having introduced readers to the principle debates on benchmarking strategies, which depend on the perspective and type of indicators used, focuses on the methodological problems related to performing consistent benchmarking analyses. Particularly, statistical methods suitable for controlling case-mix, analyzing aggregate data, rare events, and continuous outcomes measured with error are examined. Specific challenges of benchmarking strategies, such as the risk of risk adjustment (case-mix fallacy, underreporting, risk of comparing noncomparable hospitals), selection bias, and possible strategies for the development of consistent benchmarking analyses, are discussed. Finally, to demonstrate the feasibility of the illustrated benchmarking strategies, an application focused on determining regional benchmarks for patient satisfaction (using 2009 Lombardy Region Patient Satisfaction Questionnaire) is proposed

Recent proposal for the estimation of household human capital

Dagum and Slottje (2000) estimated household Human Capital (HC) as a Latent Variable (LV) proposing its monetary estimation by means of an actuarial approach. This paper introduces an improved method for the estimation of household HC as LV by means of formative and reflective indicators in agreement with the accepted economic definition of HC. The monetary HC distribution, estimated for Italian (2000) and US (2004) household, is used in a recursive causal model to explore the role of HC in macroeconomy