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Use and Editing of Administrative Data in the Business Indicators Unit, Statistics New Zealand. Blair Cardno & Vera Costa Statistics New Zealand. Overview. Introduction Summary of the Business Indicators Unit at Statistics New Zealand Collections, uses and comparisons
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Use and Editing of Administrative Data in the Business Indicators Unit, Statistics New Zealand Blair Cardno & Vera Costa Statistics New Zealand
Overview • Introduction • Summary of the Business Indicators Unit at Statistics New Zealand • Collections, uses and comparisons • Recent developments in editing • Future directions in editing • Conclusions
Summary of the Business Indicators Unit at Statistics New Zealand • Focus of the Business Indicators Unit (BIU) • Summary of the BIU administrative data collections • Customs Service trade data • Goods and Services Tax (GST) data • Local government building consents data • Electronic Transaction Data (Electronic Point of Sale data)
Collections, uses and comparisons • Customs Service Data • Used for Overseas Merchandise Trade statistics • Current collection and editing methods • Electronic files sent to Statistics New Zealand in agreed format • Files are loaded into a custom capture / edit program • Auto edits • Manual edits • Macro checks • Issues • Introduction of threshold editing
Collections, uses and comparisons cont.. • Goods and Services Tax (GST) data • Used for the Business Activity Indicator Series (BAI), supplementing surveys • Current collection, editing & imputation methods for BAI • Inland Revenue Dept extracts records once per month • The GST units matched by their IRD number to the Business Frame • Data imputation • Data editing • Issues • Uses of BAI data to supplement surveys • Used in the Manufacturing, Wholesale and Retail Trade Surveys to remove the need to survey small to medium sized businesses • Annual GST sales used as an indicator of size in some surveys
Collections, uses and comparisons cont.. • Comparisons of BIU administrative data • Providers • Customs and GST files contain a similar number of records per month (approx. 500,000). • Building consents data are provided by 74 providers (6,000 – 7,000 consents per month). • Administrative data design at source & processing for use • None of the administrative sources are specifically designed with statistical use in mind. • Customs data vs GST data vs Building Consents
Collections, uses and comparisons cont.. • Comparisons of BIU administrative data • Variable types & editing • GST data are numerical • Customs data and Building consents are a mix of numeric & categorical • Data editing approaches • “perfect data” vs “do what we can”
Recent Developments – Overseas Trade • Current situation • Editing system targets “perfect data” • Too much manual editing • Short term objectives • Methodology that: • Can be implemented easily • Has minimal impact on current editing procedures • Reduces the quality of key outputs by an amount that is acceptable • To reduce the editing effort by about 25%
Recent Developments – Overseas Trade • Proposal: entries with non-missing $ values below a specified threshold are never brought up for editor action • Analysis: impact on estimates at different country and commodity levels • Data used • Before and after editing • 2 months (Apr04, May04) • Exports and imports
Recent Developments – Overseas Trade • Impact of the amount of data manually edited
Recent Developments – Overseas Trade • No change in the current system needed • Preliminary results • Labour • Expected reduction – 2 people per year • Obtained reduction – 3 people per year • Amount of data manually edited • Before the threshold introduction – 12% • After the threshold introduction – 7%
Recent Developments – Business Activity Indicators • Methodology takes into account the nature of the data • All data to be edited is numeric • Data is available at the unit record level • Long time series for most units • No weights involved • Outliers detection • Automatic • Based on Statistical Process Control • Outliers editing • Manual / automatic • Based on selective editing • Project implementation
Future Directions - Overall editing strategy • Generalised Systems • Modular approach • Editing for numeric / categorical variables, etc • Overall strategy of Statistics NZ • Automation • Labour / cost • Consistency • Bias • Timeliness • More time for analysis • Selective / significance editing • Quality Assessment • Collections improvement
Future Directions - Challenges • What is “fitness for use”? • Different uses of the data
Conclusions • Collections at different stages of development • Emphasis on use of administrative data • Administrative data: • is crucial in producing statistical outputs • is continually investigated in Statistics NZ • Data available at different government agencies: • potential for the production of a new range of statistical information • Editing Strategy • Fundamental for the production of good quality data