Reduction of administrative burden through official statistics Feedback from the 94th DGINS Conference 25-26 September 2008, Vilnius, Lithuania

Three plenary sessions Two parallel sessions Presentation of the Lithuanian Statistical System Round table 25 papers presented and discussed 130 participants

Background - Timely and reliable information is critical for analysing, measuring and calibrating the ways in which complex economy operates The EU Commission’s action plan (January 2007) aims at reducing burdens in the EU by 25% by 2012 - Methods, priorities and resources are being adopted to the better regulation goals - Intrastat, SBS, STS, IT …. EU Commission’s action plan (January 2007) identified statistics as one of 13 priority areas for burden reduction - Code of Practice – Principal 9

Framework for improvement Increased demand for statistics, Priority setting and Coordination IMPROVED EFFICIENCY

Framework for coordination improvement - The European Statistical System Eurostat  National Statistical Institutes Inter-institutional co-operation - Sound legal frameworks at EU and national levels

Coordination at the national level Main forms of cooperation: - The use of administrative registers - Electronic data collection - e-Government Relations with the business sector Inter-institutional co-operation

- a matter of trust and quality - Reresponse burden - a matter of trust and quality - Common views Burden = Units x Time x Cost (x Frequency) Quality = f (Units, Items, Redundancy,…)

Approaches - not WHAT but HOW Process Optimisation = Burden less, Quality equal Quality Changes = Reduced Coverage in Intrastat, Choice of sampling methods, Communication  Respondents and Stackholders Reprioritisation  Reviewing (Old) settings in the frame of Decision Making Processes Innovate ways to satisfy new demands

Critical issues Sharp increase demand of information on current economic and social situation  high burden Decrease of budget Limited possibilities of reducing statistical programme Over-regulation business statistics (too many legal acts) Considerable discrepancy perceived ↔ actual (real) burden

How to reduce burden Improved contact to data providers Close contact and feed back to respondents/data providers Survey calendar Council of respondents Information material/channels Early information about incoming surveys Improved questionnaires Electronic questionnaires (and electronic usability) Standardized and simplified variables in survey forms Control of the time needed to fill in questionnaires Simplified questionnaires for small enterprises Adaptation of questionnaires to the available information in enterprises Different questionnaires for different economic sectors

How to reduce burden IT solutions and better technology Online surveys Using the electronic systems of enterprises Achievement in data transmission Improved sampling methods Improved sampling management Exemption of small enterprises (random sampling) Optimised samples

How to reduce burden Better survey design (design phase) Avoiding overlapping of surveys Rotation of units Checking of frequency Better survey monitoring (collection phase) Better co-ordination of the time of surveys Better management of data collection (monitoring) Website with information of all statistical operations Co-ordination between data producers Tunning Statistical System of large enterprices and SMEs Cooperation between Institutions Consolidated questionnaires Common projects

How to reduce burden Use of administrative registers and exploitation of existing data sources National projects Auxiliary systems of information variables from administrative data sources (chances but also risks – quality, data do not fit together, data confidentiality) Creation of statistical registers Better Regulation INTRASTAT Clear indication of future requirements

Conclusions There is no “one cure for all” Many different approaches Focuses set differently Harmonisation and co-ordination of the approaches will be necessary  global strategy? Reviewing the traditional system Entry of new actors and methods in the future Working on statistical processes Communication is a challenge

Thank you for your attention J Thank you for your attention