Auxiliary information for calibration Sample Clauses

Auxiliary information for calibration. The direct calibrated estimators described in section 4.1. make use of auxiliary information, that is of information on the domains of interest available from sources independent from the EU SILC survey, such as Censuses or Administrative archives, which may be used to improve the estimation of area­specific poverty rates. Totals are obtained from the same data sources used in the derivation of the final official weights for all the variables, except for the distribution of the population by household type within administrative regions, which can be obtained as an average of the quarterly Labour Force Survey results in 2007. Other studies (Xxxxxxx et al., 2008) have analyzed similar data sets, in which domains are given by Administrative Regions. They found evidence that poverty rates are strongly correlated with the unemployment rate. These estimates are based on the Italian Labour Force Survey (ILFS; Istat, 2003). In fact, the correlation is rather high (around 0.7). At our level of governance (Municipality and Health Society) Istat does not routinely calculate and publish estimates of unemployment rates. Istat is unlikely to provide us with the estimates of the annual average unemployment rates for our domains or at Province level in year 2007 (the income reference period). By the way, although calculated on a much bigger sample (the ILFS has an overall annual sample of around 300,000 households), estimates at the level of disaggregation we are interested in are characterized by a considerable level of uncertainty, in particular for typologies for which the rate of participation to the labour market is low. This uncertainty has to be accounted for in the analysis. We can also consider auxiliary information at our level of governance using the regional section of the National System of Accounts, the ILFS and other administrative archives as data sources. The following variables resulted to be relevant in previous poverty studies: per­capita consumption of the household sector, per­capita GDP, per­capita employee income, per­capita expenditure for leisure and culture, per­capita taxable income, share of workers/value added in the manufacturing industry, school abandonment rate, annual average unemployment rate. Note that all these variables, being estimated at our level of governance, are characterized by a level of uncertainty that may be considered in the implementation of the models to obtain the combined estimates. References Xxxxxxx, X. X. and Xxxxxx...
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