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The new multiple-source system for Italian Structural Business Statistics based on administrative and survey data Orietta Luzi , Ugo Guarnera , Paolo Righi Italian National Statistical Institute ( Istat ) Q2014 Conference - Vienna , 3-5 June, 2014. Outline.

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  1. The new multiple-source system for Italian Structural Business Statistics based on administrative and survey data OriettaLuzi, UgoGuarnera, Paolo Righi Italian National Statistical Institute (Istat) Q2014 Conference - Vienna, 3-5 June, 2014

  2. Outline - The new statistical information system «frame SBS» - The sources of the «frame SBS» - The estimation strategy - Concluding remarks and future work The new system for estimating structural economic statistics on enterprises based on the integrated use of survey data and administrative data – Istat, Rome, 11 January 2013

  3. The «frame SBS»: a multiple-source system for Italian Structural Business Statistics based on administrative and survey data Statistical information system for estimating structural economic variables on business accounts (Turnover, Purchases of goods and Services, Production Value, Value Added, … ) for small and medium enterprises based on the primary use of integrated administrative/fiscal data, “complemented” with survey data Until now, SBS for enterprises with less than 100 employees (~4.4 mln units in 2011) have been estimated based on a direct sample survey (~100,000 units) - administrative data were used as auxiliary information. The new system for estimating structural economic statistics on enterprises based on the integrated use of survey data and administrative data – Istat, Rome, 11 January 2013

  4. The sources of the «frame SBS» • Financial Statements(FS) of corporate enterprises liable to fill in the financial statement (about 800.000 enterprises each year) • The Sector Studies survey (SS), which is a Fiscal Authority survey that includes each year about 3.5 mlnenterprises with a turnover lower than 7.5 mln and greater than 30,000 euros belonging to many economic activity sectors • The Tax Return Data (Unicomodel), based on a unified model of tax declarations by legal form, and IRAP , the Italian regional tax on productive activities • The Business Register(BR). Used as population list, auxiliary source of information • The Social Security Data(SSD), which includes firm level data and employee data on wages and labor cost. Auxiliary source of information The new system for estimating structural economic statistics on enterprises based on the integrated use of survey data and administrative data – Istat, Rome, 11 January 2013

  5. The sourcesof the «frame SBS»

  6. The estimation strategy • Only the survey respondents provide information (YjS) on all the target variables Yj* (j=1,..p), based on the SBS Regulation definitions • Information on target variables Yj*, say Yji, may be available in one or more source i, on either disjoined or overlapping sub-populations • Two main steps for each source i and variable j: • harmonization of the Yjidefinition with the one described by the SBS Regulation for the corresponding Yj* • quality evaluation of harmonized Yjibased on the comparison with the corresponding YjS • Only some harmonized Yjiare considered reliable enough in terms of reported values (Main economic aggregates). In case of overlap, sources are prioritized • For the other target variables (Components of the main economic aggregates)the only reliable information is that provided by the survey resp.

  7. Main economic aggregates

  8. Coverage rate of the SME population by source and some main economic aggregates

  9. A “hybrid” estimation strategy • Main economic aggregates: model based (predictive) approach • “Mixed” unit-level mass imputation to compensate for not covered units and variables values • Components of the main economic aggregates: design based/model assisted estimation approach • Projection Estimator to obtain consistent domains estimates w.r.t. the main economic aggregates estimates

  10. Main economic aggregates: Mass imputation • Direct use of administrative and fiscal data • Choice of methods: variables relations and distributions characteristics • Predictive Mean Matching, Nearest Neighbor Donor, two-step logistic + regression models, deterministic imputation) • Avoid inconsistencies between estimates at whatever domain levels

  11. Components of the main economic aggregates: the projection estimator (*) • «Synthetic imputation» of variable values non observed in the sample based on weighted regression models estimated on the SME survey respondents (~40.000 enterprises) • Auxiliary Variables: main economic aggregates, structural information (BR) • Consistency • Among components and their reference aggregates • Between estimates at the planned SBS+SEC estimation levels • Approximately unbiased estimates at the level of model estimation domain • Trade-off between bias (high detail level) and variance (low sample size) of parameters estimates • (*) Kim, J. K. K., Rao, J. N. K. (2011). Combining data from two independent surveys: a model-assisted approach. Biometrika. No.8, pp. 1–16.

  12. Turnover C11101 C11102 C11103 C11104 C11105 C11106 C111 07 1.67% 1.05% 7.22% 9.81% 6.75% 13.62% 1.69% Purchases goods C12101 C12102 C12103 1.81% 9.36% 1.10% Purchases C12201 C12202 C12203 C12205 C12206 C12207 C12208 C12209 services (1) 3. 7 % 4.7% 5.7% 5. 3 % 5. 1 % 11.6% 3. 5 % 6. 1 % Purchases C12210 C12211 C12212 C12213 C12214 C12245 C12246 C12247 services (2) 3.00% 9.55% 5.82% 2.45% 3.06% 2.98% 9.44% 2.15% Use of third C12301 C12302 C12304 party assets 1.20% 2.44% 2.90% Other oper . C12903 C12905 charges 1.15% 6.13% CVs for some components of main aggregates (year 2011) 6

  13. Some results: survey-based vs frame-based estimates on SMEs by main economic aggregates, by size class (year 2011)

  14. Concluding remarks…. • Overcome some limitations of the current statistical production strategy (costs, burden, accuracy). Expected increase of SBS consistency over time • Higher levels of consistency between annual statistics on enterprises and National Accounts, starting from the 2011 Benchmark • … and future work • Managing unit identification problems over time (splits, fusions,…) • Assessing estimates accuracy for the main economic aggregates • Improve inferences for some components of the main economic aggregates in specific economic sectors • Consistent estimation w.r.t. the frame information in the different domains of statistics on enterprises (R&D, ICT, etc.)

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