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Use of administrative data in short term economic indicators

Use of administrative data in short term economic indicators . Statistics NZ Rochelle Barrow. Overview. Statistics New Zealand – environment current use of admin data in short term economic indicators future use of admin data. Statistics NZ – current environment.

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Use of administrative data in short term economic indicators

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  1. Use of administrative data in short term economic indicators Statistics NZ Rochelle Barrow

  2. Overview • Statistics New Zealand – environment • current use of admin data in short term economic indicators • future use of admin data

  3. Statistics NZ – current environment • large emphasis on respondent load • Minister of Statistics is also Minister of Small Business • efficiency

  4. Current use of admin data • Quarterly Manufacturing Survey • KAU based • Operating income, purchases, raw material stocks, finished goods stocks, additions and disposals of assets • Quarterly Wholesale Trade Survey • KAU based • Operating income, raw material stocks and finished goods stocks • Monthly Retail Trade Survey • GEO (geographic location/establishment) based • Monthly sales, quarterly stocks

  5. Type of admin data used • data sourced from Inland Revenue Department • registrations and deregistrations • goods and services tax returns • employer monthly schedules • data received monthly • approximately 6 weeks after the end of the reference period

  6. Data manipulation • admin data not equal to survey collected data • match data to statistical model • manipulation of GST data • Group apportionment • Estimation and apportionment of non monthly data • Estimation of missing data result = a monthly series of GST sales and purchases by enterprise

  7. How the data is used • update population details • stratify enterprises • replace direct surveying of small business Result = 25 percent decrease in respondent load

  8. Updating population details • registrations and deregistrations • update the dynamic business frame • business rules used to determine which enterprises can be updated directly from admin data

  9. Stratify businesses • stratification by industry (ANZSIC) • stratification variables: • Annualised GST • Rolling mean employment count • use of two stratification variables has improved the efficiency of the sample design – decreasing the required sample size Annualised GST Full coverage sample tax Rolling mean EC

  10. Manufacturing Survey (Mar 04 quarter)

  11. Retail Trade Survey (April 04 month)

  12. Replace direct surveying of small business • desire to reduce respondent load • problems encountered

  13. Potential use of admin data - background information • Electronic Funds Transfer at Point of Sale (EFTPOS) • NZ has a relatively high level of debit and credit card useage • businesses with electronic terminals deal directly with banks • banks then engage one of two switching houses to process EFTPOS transactions • the switching houses are owned (jointly) by the banks • both switching houses have provided SNZ with data • currently confirming scope and classifications e.g. regional and arranging ongoing supply of data

  14. Data requested from switching houses MC = industry code RG = region • includes internet transactions • excludes overseas transactions

  15. Analysis • EFTPOS data for recent periods closely track movements in the Retail Trade Survey

  16. Advantages and potential uses • timeliness – available 5 - 10 days after the end of the reference period • more robust small domain estimates e.g. regional • trading day adjustment analysis • possible reduction in respondent load (if Retail Trade Survey moves to quarterly survey) • other analysis and validation e.g. money spent by overseas visitors

  17. Issues relating to EFTPOS data • penetration of EFTPOS use • differing degrees of card useage by storetype e.g supermarkets vs motor vehicle retailing • one bank already produces estimates based on their EFTPOS data

  18. Conclusion • SNZ makes extensive use of admin data in short term indicators • benefits include significant reductions in respondent load • considerable opportunities exist with respect to EFTPOS data

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