Claudia Pigini

A test for bivariate normality with applications in microeconometric models

In this paper, we propose a test for bivariate normality in imperfectly observed models, based on the information matrix test for censored models with bootstrap critical values. In order to evaluate its properties, we run a comprehensive Monte Carlo experiment, in which we use the bivariate probit model and Heckman sample selection model as examples. We find that, while asymptotic critical values can be seriously misleading, the use of bootstrap critical values results in a test that has excellent size and power properties even in small samples. Since this procedure is relatively inexpensive from a computational viewpoint and is easy to generalise to models with arbitrary censoring schemes, we recommend it as an important and valuable testing tool.

Testing for State Dependence in Binary Panel Data with Individual Covariates by a Modified Quadratic Exponential Model

ABSTRACT 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.

Employment Condition, Economic Deprivation and Self-Evaluated Health in Europe: Evidence from EU-SILC 2009–2012

Background: The mixed empirical evidence about employment conditions (i.e., permanent vs. temporary job, full-time vs. part-time job) as well as unemployment has motivated the development of conceptual models with the aim of assessing the pathways leading to effects of employment status on health. Alongside physically and psychologically riskier working conditions, one channel stems in the possibly severe economic deprivation faced by temporary workers. We investigate whether economic deprivation is able to partly capture the effect of employment status on Self-evaluated Health Status (SHS). Methods: Our analysis is based on the European Union Statistics on Income and Living Conditions (EU-SILC) survey, for a balanced sample from 26 countries from 2009 to 2012. We estimate a correlated random-effects logit model for the SHS that accounts for the ordered nature of the dependent variable and the longitudinal structure of the data. Results and Discussion: Material deprivation and economic strain are able to partly account for the negative effects on SHS from precarious and part-time employment as well as from unemployment that, however, exhibits a significant independent negative association with SHS. Conclusions: Some of the indicators used to proxy economic deprivation are significant predictors of SHS and their correlation with the employment condition is such that it should not be neglected in empirical analysis, when available and further to the monetary income.

A finite mixture latent trajectory model for hirings and separations in the labor market

We propose a finite mixture latent trajectory model to study the behavior of firms in terms of open-ended employment contracts that are activated and terminated during a certain period. The model is based on the assumption that the population of firms is composed by unobservable clusters (or latent classes) with a homogeneous time trend in the number of hirings and separations. Our proposal also accounts for the presence of informative drop-out due to the exit of a firm from the market. Parameter estimation is based on the maximum likelihood method, which is efficiently performed through an EM algorithm. The model is applied to data coming from the Compulsory Communication dataset of the local labor office of the province of Perugia (Italy) for the period 2009-2012. The application reveals the presence of six latent classes of firms.