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Orietta Luzi, Giulio Perani, Giovanni Seri Italian National Statistical Institute (Istat)

The use of administrative fiscal data for the production of R&D statistics in Italy. Orietta Luzi, Giulio Perani, Giovanni Seri Italian National Statistical Institute (Istat) {luzi,perani,seri}@istat.it Q2010 Helsinki “European Conference on Quality in Official Statistics”.

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Orietta Luzi, Giulio Perani, Giovanni Seri Italian National Statistical Institute (Istat)

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  1. The use of administrative fiscal data for the production of R&D statisticsin Italy Orietta Luzi, Giulio Perani, Giovanni Seri Italian National Statistical Institute (Istat) {luzi,perani,seri}@istat.it Q2010 Helsinki “European Conference on Quality in Official Statistics” Helsinki 4-6 May 2010

  2. Collecting R&D statistics: key issues Q2010 Helsinki • A statistical problem: detecting and measuring a phenomenon (business R&D) involving a very small fraction of units in a large population. • Undertaking R&D activities is most frequent among large enterprises and those belonging to R&D-intensive industries but the choice of performing R&D depends on a number of technical and economic opportunities.

  3. The Italian business R&D survey (1) Q2010 Helsinki • Annual survey, census based, since 1963. • Population frame: “potential R&D performers” • (to identify all potential R&D performers it is extremely difficult because of the high heterogeneity in business R&D strategies and practices) • Internal sources • Previous Istat R&D surveys • Other Istat business surveys with R&D-related questions • Istat official statistical business register • The exploitation of administrative data • (Since the 1990s, Istat has been collecting administrative data) • Italian Register of R&D performing institutions • National and EU funding to research projects • Patent databases • Business reports

  4. The Italian business R&D survey (2) Q2010 Helsinki • Fiscal data from 2004. • Around 27,000 “potential R&D performers” (including 200 largest performers). • Administrative data to identify potential R&D performers, not for imputation. • Non-responses treated by “cold-deck imputation” from previous R&D or other business surveys.

  5. R&D tax credits in Italy (1) Q2010 Helsinki • Since the fiscal year 2007, Italian businesses have been allowed to apply for a corporate tax relief proportional to their own total R&D expenditure: • 10 per cent for investments on own R&D projects • 40 per cent for investments on R&D projects developed in co-operation with universities or public R&D institutes. • A maximum of 50 million Euros of R&D expenditure can be declared by a single enterprise for each fiscal year until 2013.

  6. R&D tax credits in Italy (2) Q2010 Helsinki The definitions used by the Italian legislators in establishing such R&D tax reduction scheme follow more the European Commission (EC) standards than those recommended by the OECD’s Frascati Manual (FM):

  7. Relationships between variables Q2010 Helsinki

  8. Relationships between variables Q2010 Helsinki

  9. ISTAT R&D survey database ~ 4,568 pre-checked records Tax Agency database ~12,000 records Matching statistical and tax credit data Q2010 Helsinki ~ 5000 new potential R&D performers MATCHING = 2,897 matched records

  10. Study results (1) Q2010 Helsinki Total intra+extra-mural R&D expenditure from the R&D survey: C312+C409 (log scale) Total R&D expenditure from the fiscal source – RS15 (log scale) OUTLIERS’ IDENTIFICATION AND CLEANING = 2,813 matched records

  11. Study results (2) Q2010 Helsinki

  12. Study results (2) Q2010 Helsinki

  13. Matching statistical and tax credits data Q2010 Helsinki • Objective: Preliminary assessment of the capability of fiscal data to predict non responses in R&D survey data • Approach • Simulation of a pre-defined % of missing values in each the R&D items (MCAR assumption) • Robust regression models: fiscal variables as auxiliary independent variables (log scale) • Evaluation of the quality of predicted data at aggregate level (for the 3 models): % Relative bias of the estimated R&D variable’s total on all subsets (1,000 iterations)

  14. Study results (3) Q2010 Helsinki

  15. Study results (3) Q2010 Helsinki

  16. Conclusions and future activities Q2010 Helsinki • Future extensive use of tax credits data for statistical purposes (e.g. edit/validate the results of R&D surveys, predict non responses, keeping updated the Istat “register” of R&D performers) • Further investigations on the coherence of definitions and on the quality of the fiscal source (coverage and data reporting) • Need for monitoring the stability of the fiscal source (availability on a regular basis, coverage, financial sustainability) • Using the administrative tax data to design a sample R&D survey on SMEs • Developing models for predicting R&D items not available from the fiscal source

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