Italian situation in the following areas: Meeting of Task Force on Small and Medium Sized Enterprise Data (SMED)   5th February 2015, 10:00-16:00 Italian situation in the following areas: - introduction of additional size class breakdown for one-person enterprises - inclusion of all economic sectors - breakdown by legal form for enterprises ; - feasibility of regional breakdowns Giovanni Alfredo Barbieri - Giampiero Siesto

General remarks on the Italian SBS data (1 of 2) Traditionally, Italian SBS data have been compiled by combining the estimates from the survey on small and medium sized enterprises with less than 100 persons employed (PMI survey) with those from the census survey on enterprises with 100 persons employed and more (SCI survey). Both surveys use administrative sources to integrate total non-responses. Starting from SBS2012 Istat has combined administrative sources with survey data aiming at reducing the sampling error and at improving data quality. The innovation in the production process of the SBS2012 data involves the enterprises with less than 100 persons employed: variable estimates are based on exhaustive administrative sources for the main economic variables (turnover, purchases of goods and services, value added, personnel costs, etc.), combined with other economic variables not available from administrative sources, which are estimated exploiting PMI direct survey data by using either weighted regression estimators or calibration (e.g. investments). The structural variables (number of persons employed, number of employees, economic activity, administrative region) come from the business register of the active enterprises (Asia). 2

General remarks on the Italian SBS data (2 of 2) The administrative sources (Financial Statements of corporate enterprises from Chambers of Commerce, Sector Studies survey and Tax Return data from the fiscal authority, and Social security data) have been analysed in terms of both coverage of the SBS target population as listed in the Business Register, and available variables. A comparative analysis of the variables observed in each administrative source and in the PMI survey has led to the integrated use of the relevant administrative sources according to a specific “hierarchy”. Such hierarchy is based on how the variable definitions are close to the SBS ones and on the reliability of the sources themselves (stability, availability, completeness, etc.). Target variables not available in the administrative sources have been estimated through massive imputation, using a mixed approach, depending on the coverage rate of the target variables to be estimated: classical predictive model-based approaches have been used for estimating high coverage variables, while models based on SME data have been adopted for estimating the remaining variables. In this way a multidimensional micro data matrix has been built (called “FrameSBS”), containing the Business Register variables and the economic variables for all the SBS population units. 3

Introduction of additional size class breakdown for one-person enterprises In accordance with SBS regulation, for trade and services we already produced series with a breakdown at 3 digits Nace and size class of persons employed 0-1 (size code=01), while in industry and construction the lower size class of persons employed is 0-9 (size code=02). With our FrameSBS we can produce data for the size class 0-1 person employed also for all the economic activities but only for a limited number of SBS variables (for example not for investments). The obstacles to introducing the additional size class 0-1 persons employed in all the economic activities are the necessity of increasing the sample size and the expected greater complexity in the confidentiality treatment. 4

Inclusion of all economic sectors The field of observation of FrameSBS and of the PMI and SCI surveys is wider than that of the SBS regulation, as the former covers also market activities in Nace P (Education), Q (Human health and social work activities), R (arts, entertainment and recreation) and S96 (other personal services activities). The obstacles that other countries could find in including other economic activities are, as in the previous area, the necessity of increasing the sample size and the expected greater complexity in the confidentiality treatmen. Another difficulty we envisage might be the classification of the enterprise as market/non market. Agriculture and the financial sector are excluded in our analysis. Information about SME are derived from SBS data, in which the enterprises are classified only according to the number of persons employed. No evaluation is made in relation to turnover or financial statements or links with other enterprises. 5

Breakdown by legal form for enterprises The availability of micro level information on enterprises allows to produce data according to different criteria of disaggregation, including by legal form. However, not all SBS variables (i.e. investments) obtained through this source (FrameSBS) can be broken down by legal form. Moreover, the PMI survey does not provide estimates by legal form (this estimate is not foreseen in the sample design). The obstacles to introducing the legal form in the sample design are represented by a likely increase in the statistical burden on the enterprises (increasing the number of enterprises included in the sample and therefore higher costs for NSI) and additional complexity of the estimation phase that already includes the estimation of data for Nace at 4 digits, Nace at 3 digits by size classes of persons employed and Nace at 2/3 digits by regions Nuts2. In addition, also the data by legal form should be treated for statistical confidentiality by connecting all the other series previously treated (increase in complexity). 6

Feasibility of regional breakdowns Regional SBS data are produced considering the enterprises with less than 100 persons employed (PMI survey) with reference to the main economic activity and to the headquarters (region). For the enterprises with 100 persons employed and more (SCI survey) we take into account the economic activity in accordance with the KAU concept and the region where the production is actually realized. The obstacles to introducing a breakdown of regional data by size class (persons employed) have several implications for the kind of statistical unit to be taken into account. If by enterprise, we will not have a good representation of the regional economy, while considering local KAU we will have a better representation of regional production but the size classes of persons employed would not be consistent with the data produced by enterprises in accordance with Nace at 3 digits and size class of persons employed. In addition, the breakdown of regional data by size class must be introduced in the sample design (heavier burden on the enterprises and costs for the NSI), and would add complexity to the estimation phase and to the treatment of confidentiality. For the latter, the number of cells suppressed could be increased significantly in SBS. 7