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For every municipality I c i with no housing insured (it happens for some small municipalities), we replace HT ht with HT ht , computed over all the HT ht insured housing units of the province I c i SB I P i sb to which municipality I c i belongs. For municipality I c i , if I N i SB I c i sb is the total number of housing units, an estimate I v i SB I c i sb of the housing stock's value is: HT ht Graph where I k i is a scaling factor to obtain HT ht billion euros. [Extracted from the article]

We examine two sources of bias for the Bank of Italy's panel business survey of Industrial and Services Firms: 1) the bias caused by panel attrition; and 2) the bias created by delays in the distributional data on the reference population, needed for computing the survey weights. As for the first source of bias, the estimates strongly dependent on big firms' values are less affected by panel attrition than those representing firms' average behavior, independent of their sizes. Positive economic results make it easier to enroll new firms in the survey, in order to replace firms dropping out because of bad economic performances. However, the economic results of new entrances become more aligned to those of the population, once they enter the sample. A very different result emerges for the second source of bias, since, when the population size is highly variable, the information delays produce a bias for the estimates influenced by the contribution of great firms, but the effect is negligible for the estimates not dependent on firm size. [ABSTRACT FROM AUTHOR]