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54 Volume LXIII nn. 3-4 – Luglio-Dicembre 2009 Tabella A1 – Stime del modello logit Dimensione Variabile Odd Ratio Std. Err. P>z Numero di stanze disponibili 1.2117 0.0231 0.0000 AB Possibilità di tenere riscaldata la casa 1.7332 0.1140 0.0000 Costo totale per l'abilitazione 0.9995 0.0000 0.0000 Titolo di godimento dell'abitazione 1.0892 0.0272 0.0000 Casa buia 1.1959 0.0807 0.0080 Casa con problemi di umidità 1.2527 0.0615 0.0000 Arretrati nel pagamento di bollette 1.8966 0.1353 0.0000 Incidenza costo abitazione sul bilancio familiare 2.0710 0.0877 0.0000 Difficoltà a comprare cibo 0.9956 0.0937 0.9630 Difficoltà a comprare vestiti 1.9135 0.1070 0.0000 Difficoltà a pagare le spese per malattie 1.6922 0.1150 0.0000 (D)EF Difficoltà a pagare le spese per la scuola 1.1601 0.0439 0.0000 Difficoltà a pagare le tasse 1.2197 0.0610 0.0000 Possibilità di fare una settimana di vacanza 4.5952 0.2032 0.0000 all'anno Possibiltà di mangiare carne e pesce almeno due 1.6816 0.1335 0.0000 volte a settimana Debiti per acquisto beni di consumo 1.3905 0.0748 0.0000 Posseso automobile 1.5253 0.0864 0.0000 Possesso telefono fisso 1.2549 0.0640 0.0000 DUR Possesso VHS 1.2541 0.0618 0.0000 Accesso ad internet 1.6073 0.0897 0.0000 Possesso di parabola 1.0951 0.0567 0.0790 Condizione professionale 1.8242 0.2139 0.0000 - lavoratore dipendente - lavoratore autonomo 2.7873 0.3590 0.0000 - pensionato 2.0014 0.2547 0.0000 - altro 1.5765 0.2032 0.0000 Livello di istruzione SD - Media inferiore 1.0850 0.0837 0.2900 - Media superiore 1.2227 0.1082 0.0230 - Laurea 1.3983 0.1277 0.0000 - Titolo di studio superiore alla laurea 2.2058 0.2821 0.0000 1.2890 0.0617 0.0000 Sesso Età 1.1902 0.0389 0.0000 Area Dummy territoriale 1.1044 0.0463 0.0180 Pseudo R2 0.345 Famiglie correttamente classificate 0.8256 SUMMARY Using a logit model, the paper aims to analyse and measure living standards of Italian households by means of EU-SILC dataset for the year 2005. Three dimensions of living standards have been considered, i.e. financial (in)balance, housing conditions and the possession of durable goods. The results show the relevant dimension of living standards is the financial (in)balance one. Moreover, by a joint analysis of living standards and socio-demographic characteristics (i.e. sex, age, education and professional labour status) different household profiles have been depicted. __________________________ Maria Francesca CRACOLICI, Assegnista di Ricerca, Università di Palermo. Miranda CUFFARO, Professore Ordinario di Statistica Economica, Università di Palermo. Francesca GIAMBONA, Assegnista di Ricerca, Università di Palermo.
Rivista Italiana di Economia Demografia e Statistica Volume LXIII nn. 3-4 – Luglio-Dicembre 2009 THE EFFECTS OF IMPUTED RENT ON INCOME, POVERTY AND INEQUALITY IN FOUR EUROPEAN COUNTRIES Andrea Cutillo, Davide Di Laurea • 1. Introduction The income position of individuals and households is determined by monetary earnings, but also influenced by non-monetary components and in-kind transfers. The inclusion of non-monetary earnings is recommended in international standards in order to improve the comparability of poverty and economic inequality of people living in different situations. In this paper we focus on a particular type of non-cash income, the imputed rent (IR). Following the European Commission Regulation n. 1980/2003, “the imputed rent refers to the value that should be imputed to all the households that do not pay full rent for their main residence, i.e. a market price rent, either because they are owner-occupiers or they live in accommodation rented at lower price than the market price, or because their accommodation is provided rent-free”. There is a large agreement about the importance of integrating this component into income measures (ILO, 2003; Canberra Group, 2001). We focus on the distributional impact of imputed rent in four European Countries: France, Italy, Spain and United Kingdom. Even if the attention on imputed rent has raised in the last years, little attention has been given to the international comparison (see, for instance, Frick et al. (2008)); in addition there is not yet a standardised calculation method, as suggested by the Canberra Group (2001) and by Smeeding and Weinberg (2001): “if net imputed rent is included in income, one must be careful that it is measured in a way that leads to greater international standardization instead of nation-specific measures of its value”. We use Eu-Silc (European Community Statistics on Income and Living Conditions) 2006 microdata: this survey is harmonized across Countries, and this enables us to homogeneously estimate imputed rent and compare the results. 2. Estimation methods The IR must be included for all households that do not report paying full rent: those living in owned dwellings and households living in accommodation rented at a lower • This note, though is the result of a close collaboration, was specifically elaborated as follows: par. 1, 2 and 4 by A. Cutillo; par. 3 and 5 by D. Di Laurea
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INFORMAZIONI PER GLI AUTORI REGOLE
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SOCIETÀ E RIVISTA ADERENTI AL SIST
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