Giorgio Alleva

Estimating the Probability of Moonlighting in Italian Building Industry

It’s well known that black market economy and especially undeclared work undermines the financing of national social security programs and hinders efforts to boost economic growth. This paper goes in the direction of shedding light on the phenomenon by using statistical models to detect which companies are more likely to hire off the books workers. We used database from different administrative sources and link them together in order to have an informative system able to capture all aspects of firms activity. Afterward we used both parametric and non parametric models to estimate the probability of a firm to use moonlighters. We have chosen to study building industry both because of its importance in the economy of a country and because its a wide spread problem in that sector

Estimates based on preliminary data from a specific subsample and from respondents not included in the subsample

Various approaches to obtaining estimates based on preliminary data are outlined. A case is then considered which frequently arises when selecting a subsample of units, the information for which is collected within a deadline that allows preliminary estimates to be produced. At the moment when these estimates have to be produced it often occurs that, although the collection of data on subsample units is still not complete, information is available on a set of units which does not belong to the sample selected for the production of the preliminary estimates. An estimation method is proposed which allows all the data available on a given date to be used to the full-and the expression of the expectation and variance are derived. The proposal is based on two-phase sampling theory and on the hypothesis that the response mechanism is the result of random processes whose parameters can be suitably estimated. An empirical analysis of the performance of the estimator on the Italian Survey on building permits concludes the work.