In this paper, spatial shift-share decomposition is analysed when applied to Italian data on regional business change at plant level, over the period [2004][2005][2006][2007][2008][2009]. A new type of spatial decomposition, which looks more effectively at neighbourhood influence, is introduced here. Notable results emerge from the empirical investigation. First, it can be seen that the spatial level of aggregation greatly affects results. Second, evidence of neighbourhood advantage in the Southern NUTS 3 regions is found, together with opposite results for the Central-Northern NUTS 3 regions. Finally, evidence of positive industrial mix effects is only found in CentralNorthern Italy.JEL classification: C21, L26, R12

The quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several years. However, different sources of administrative data are not expected to each have the same population coverage, so that estimating the true population size from the collective set of data poses several methodological challenges that set the problem apart from a classical capture-recapture setting. In this article, we consider two specific aspects of this problem: (1) misclassification of the units, leading to lists with both overcoverage and undercoverage; and (2) lists focusing on a specific subpopulation, leaving a proportion of the population with null probability of being captured. We propose an approach to this problem that employs a class of capturerecapture methods based on Latent Class models. We assess the proposed approach via a simulation study, then apply the method to five sources of empirical data to estimate the number of active local units of Italian enterprises in 2011.

This article contributes to the literature on firm demography and regional development in at least three different ways. First, consumption, rather than employment, which is the most common variable seen in literature, is used to measure the impact of firm demography on regional development. Second, while the literature is mainly focused on the relationship between new business formation and regional development, we investigate both entry and exit flows of firms. Third, we decompose each of these flows into spatial and sectoral components. The empirical investigation looks at the Italian regions with reference to the period 2004–2009. Results seem to be substantially divergent between the South and the rest of Italy.

Recently, a method was proposed that combines multiple imputation and latent class analysis (MILC) to correct for misclassification in combined data sets. A multiply imputed data set is generated which can be used to estimate different statistics of interest in a straightforward manner and can ensure that uncertainty due to misclassification is incorporated in the estimate of the total variance. In this article, MILC is extended by using hidden Markov modeling so that it can handle longitudinal data and correspondingly create multiple imputations for multiple time points. Recently, many researchers have investigated the use of hidden Markov modeling to estimate employment status rates using a combined data set consisting of data originating from the Labor Force Survey (LFS) and register data; this combined data set is used for the setup of the simulation study performed in this article. Furthermore, the proposed method is applied to an Italian combined LFS-register data set. We demonstrate how the MILC method can be extended to create imputations of scores for multiple time points and thereby show how the method can be adapted to practical situations.

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