Abstract
National accounts statistics are the result of the integration of several data sources. At present, in Italy, sample surveys data on households income are not included in the estimation process of national accounts aggregates. In this paper we investigate the possibility of using such data within an independent estimate of GDP, based on the income approach. The aim of this paper is to assess whether (and to what extent) sample survey microdata on household income may contribute to the estimate of GDP. To this end, surveys variables are recoded and harmonized according to the national accounting concepts and definitions in order to point out discrepancies or similarities with respect to national accounts estimates. The analysis focuses particularly on compensation of employees. Applications are based on the EU statistics on income and living conditions and on the Bank of Italy survey on income and wealth.
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Notes
- 1.
In the Italian national accounts the Household sector is split into two sub-sectors, namely producer households and consumer households. Particularly producer households include non financial unincorporated enterprises with 5 or less employees and unincorporated auxiliary financial activities with no employees. In the allocation of primary income account, a quota of mixed income moves from producer households to consumer households. This income is supposed to be used by households for consumption and saving.
- 2.
Shiw data can be downloaded from the Bank of Italy web site.
- 3.
IT-SILC XUDB 2007–May 2009.
- 4.
To compare data correctly it is necessary to estimate national accounts income components net of taxes and social contributions. We assume a proportional taxation on all income components. Actually, a microsimulation model should be used to correctly allocate taxation among different income categories.
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Coli, A., Tartamella, F. (2012). Integrating Households Income Microdata in the Estimate of the Italian GDP. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_9
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DOI: https://doi.org/10.1007/978-3-642-21037-2_9
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