1,721,002 research outputs found
Inequality and higher education in Italy. The distributive impact of fees and subsidies to academics
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The recent reforms of the Italian personal income tax: distributive and efficiency effects
Full text linkEstimating measures of multidimensional poverty in Stata
Full text linkThis paper describes the multidimensional poverty measures developed by Alkire and Foster (2011) and shows how they can be computed in Stata with the command mpi
Imputation of missing expenditure information in standard household income surveys
Full text linkThe aim of this paper is to present a new methodology for dealing with missing expenditure information in standard income surveys. Under given conditions, typical imputation procedures, such as statistical matching or regression-based models, can replicate well in the income survey both the unconditional density of household expenditure and its joint density with a set of socio-demographic variables that the two surveys have in common. However, standard imputation procedures may fail in capturing the overall relation between income and expenditure, especially if the common control variables used for the imputation have a weak correlation with the missing information. The paper suggests a two-step imputation procedure that allows reproducing the joint relation between income and expenditure observed from external sources, while maintaining the advantages of traditional imputation methods. The proposed methodology suits well for any empirical analysis that needs to relate income and consumption, such as the estimation of Engel curves or the evaluation of consumption taxes through micro-simulation models. An empirical application shows the makings of such a technique for the evaluation of the distributive effects of consumption taxes and proves that common imputation methods may produce significantly biased results in terms of policy recommendations when the control variables used for the imputation procedure are weakly correlated with the missing variable
On the role of unobserved preference heterogeneity in discrete choice models of labor supply
Full text linkThe aim of this paper is to analyse the role of unobserved preference heterogene- ity in structural discrete choice models of labor supply. Within this framework, unobserved heterogeneity has been estimated either parametrically or nonpara- metrically through random coefficient models. Nevertheless, the estimation of such models by means of standard, gradient-based methods is often difficult, in particular if the number of random parameters is high. For this reason, the role of unobserved taste variability in empirical studies is often constrained since only a small set of coefficients is assumed to be random. However, this simplification may affect the estimated labor supply elasticities and the subsequent policy pre- scriptions. In this paper, we propose a new estimation method based on an EM algorithm that allows us to fully consider the effect of unobserved heterogeneity nonparametrically. Results show that labor supply elasticities and policy prescrip- tions do change significantly only when the full set of coefficients is assumed to be random. Moreover, we analyse the behavioural effects of the introduction of a working-tax credit scheme in the Italian tax-benefit system and show that the magnitude of labor supply reactions and the post-reform income distribution can differ significantly depending on the specification of unobserved heterogeneity
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