Metodologie per la stima degli affitti figurativi ed impatto sulla distribuzione del reddito
In the relevant literature it is well established that imputed rents – a non-monetary income source for all households whose living accommodation is not rented at a market price - has to be accounted for in order to provide a more accurate and comparable measure of the total disposable household income. A more controversial issue is about the choice among distinct estimation approaches and how to operationalise them. This paper aims to analyse the impact of different estimation methods for imputed rents on the distribution of disposable income as well as on inequality measures, using data from EUSILC. Subjective rents – self-assessed values by the respondents - are compared with predicted values coming from econometric techniques, i.e. an hedonic regression model,being both methods coherent with the ‘rental equivalence value’ approach. Three main issues are assessed: i) pros and cons of each method; ii) if the tenure discount (the length of tenure), which turns to be a relevant factor in determining the observed values of the market rents, has to be taken into explicit account in predicting imputed rents; iii) how to prevent biases on model predictions eventually arising if a selection process between tenants at a market price and households living with different accommodation occurs.
A Mixed Approach for Data Fusion of HBS and SILC
This paper presents an effective procedure for matching data from the Italian Household Budget Survey (HBS) and the Survey on Income and Living Conditions (SILC), carried out by the Italian National Statistical Institute. In recent years, there is an emerging consensus on the need for distributional measures of well-being as a joint function of income and consumption, that should be jointly explored for explaining the households’ economic conditions. However, the Italian National Statistical Institute does not maintain a unified database on income and expenditures for consumption. One of the most widespread approaches for fusing data is to use statistical matching techniques, which are based on the Conditional Independence Assumption. This assumption implies that the variables of interest are independent, given a set of common auxiliary variables with strong explicative power on income and consumption. Between the auxiliary variables, we also exploit suitable variables which are proxy of income, thus permitting to impute income on HBS, and proxy of consumption, thus permitting to impute consumption on SILC. The joint distribution of the proxies can be considered as a proxy of the joint distribution between income and consumption. Moreover, through the use of two synthetic measures, we reduce the dimensionality of the set of auxiliary variables, simultaneously preserving as much as possible of the available common information and the correlation structure between the auxiliaries and income, as observed in SILC, and between the auxiliaries and consumption, as observed in HBS. The contribution to the existing literature is various. In particular, we obtain a pooled sample with income and consumption, while previous attempts aimed at imputing only one of the two variables of interest in one survey. Second, the uncertainty deriving from the lack of information on the joint distribution of income and consumption is reduced with respect to previous works. Finally, the use of proxies, the high quality of the other auxiliary variables, as well as the particular mixed-mode matching procedure, permit to take advantage of all the available information reducing it to only two dimensions, so that the Conditional Independence can be considered a mild assumption. Keeping in mind that an effective matching for also imputing wealth, that is the third pillar of the households’ economic wellbeing, is yet to come, the joined dataset could permit several types of analyses on economic inequalities and fiscal policies.
Recovering the frontier state : war, ethnicity, and state in Afghanistan
Chapter 1 Table of Contents Chapter 2 1 Introduction Chapter 3 2 Ethnicity, Political Power, and Fragmentation Chapter 4 3 Rise of the Taliban and Civil War Chapter 5 4 The United States and War on Terrorism Chapter 6 5 Restructuring the Afghan State Chapter 7 6 Political Economy of Drugs and Warlordism Chapter 8 7 Afghanistan and Neighboring States Chapter 9 8 Conclusion Chapter 10 Bibliography