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An Evaluation of Changes to the Universe Extraction for Current Business Surveys at the U.S.

Census Bureau

Author: Carol S. KingPresenter: Ruth E. Detlefsen

Service Sector Statistics DivisionU. S. Census Bureau

June 20, 2007

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Business Sample Revision

• Approximately once every five years

• For retail, wholesale, and services sectors

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Why a Business Sample Revision (BSR)

• To ensure that samples remain representative of the retail, wholesale, and services universes

• To redistribute burden for small and mid-size firms currently in the samples

• To introduce changes such as new industry codes, coverage expansion, etc.

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Topics

• Changes made to the edit process

• Efforts to improve edit bounds

• Introduction of the Edit Tracking System

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Business Register (BR)

Business Register (BR) - master business list of employer firms and establishments contains:

• administrative record data

• Economic Census data

• current survey data

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Universe Extraction Process• Extract from the BR all retail, wholesale and services

establishments

• Assign a NAICS-derived sampling recode to each inscope establishment

• Compute an estimate of each establishment’s annual sales or receipts, measure of size (MOS)

• Edit, review and correct the extracted data

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Data Edits - Why Edit the Data?

• To find systematic problems in the data or input files

• To review data used to compute the establishment measure of size

• To review establishments in industries that may have unique reporting or classification issues

• To find data problems for individual cases that if not corrected would adversely effect sample sizes

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Editing of Data in Prior Universe Extractions

• Thirteen edits done at the establishment level

• Edits tested Census and administrative data

• Edit failures assigned a priority for review

• Approximately 132,000 edit failures in BSR-2K

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Editing for BSR-06

• Performed edits at both the establishment and sampling unit level

• Reviewed establishment failures only if sampling unit failed

• Provided an interactive correction system

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Sampling Unit Edits for BSR-06

• Identified noncertainty sampling units that increased the sample size by one or more units. This increase was called the effect.

• Identified certainty units that had MOS / β * payroll out of line with other certainty units in the same sampling recode.

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Noncertainty Effect Edit

Edit failures were grouped into three categories:

• Highest priority – unit added 15 or more units to the sample size

• Next highest priority – unit added 5 to 14 units to the sample size

• Lowest priority – unit added 1 to 4 units to the sample size

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Maximum Effect for Selected Sampling Recodes

Trade Sampling Recode Initial Week 4 Week 7

Services 621490 – Other Individual and

Family Services

640 640 4

Services 523920 – Portfolio Management 639 127 32

Services 561510 – Travel Agencies 316 1 0

Retail 444130 – Hardware Stores 634 633 18

Retail 722410 – Drinking Places 106 106 106

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Certainty Edit• Used an Hidiroglou-Berthelot (H-B) edit for

certainty unit outliers

• Delineated three regions away from the acceptance region

• Failures in furthest region were given review priority

• Failures in other regions were reviewed as time permitted

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BSR-06 Edit Failures• Total establishment failures – 126,000 (132,000 for BSR-2K)

• Sampling unit failures - 3,769

• Noncertainty edit failures – 2,637 (0.07% of all noncertainty sampling units)

• Certainty sampling units - 1,132 (5% of all certainty units)

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Review and Correction of Edit Failures

• Establishments of sampling unit edit failures were reviewed based on the priority assigned from the establishment edits.

• Corrections were made as needed.

• If no correction was made, sampling unit was marked so it would not appear as a failure in subsequent edit runs.

• Edits were rerun and effects recomputed.

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Sample Sizes Before and After Data Editing

Survey

Sample Size Before Data Editing

Sample Size After Data Editing

Percent Change in Sample Size

MRTS 13,460 12,384 -8.0%

MWTS 6,834 6,556 -4.4%

SAS 70,710 61,177 -13.5%

90,554 80,117 -12.0%

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Ratio Edit Bounds in Previous BSR

Ratio BSR-2K Upper

Bound

BSR-2K Lower

Bound

Administrative Payroll/ Census

Payroll 99th percentile 1/99th percentile

Census Receipts/ β * Census

payroll 99th percentile 1/99th percentile

Census Inventory/Census

Receipts

10 0.005

MOS/Census Receipts 10 1/10

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Review of Ratio Edit Bounds

• For BSR-06,

– Plain Vanilla (PV) was examined for determining ratio bounds

– establishments were grouped by whether singleunit or multiunit establishment within the sampling recode

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Establishment Edit Bounds for BSR-06

• PV used for the ratios:– Census Receipts / β * Census Payroll – Census Inventory / Census Receipts

• BSR-2K method used for the ratios:– Administrative Payroll / Census Payroll– MOS / Annualized Census Receipts

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Edit Tracking System

• Which establishments were corrected

• When corrections were made

• Which variables were corrected

• The magnitude of the corrections.

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Some Results from the Tracking System

• Corrections made throughout the 7-week period

• Fifteen correctable fields. Most corrections to six fields.

• Number of establishments with at least one change as a percent of the total number of establishments was small.

• Changes made impacted the sample sizes.

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Conclusions

• Satisfied with the sampling units edits and resulting reduction in sample sizes.

• Improved our edit bound determination process but would like to see if more improvements can be made.

• Will use the output of the edit tracking system to determine what improvements can be made to the extraction process for the next sample revision.

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Contact Information

Author: Carol S. King Carol.Steadman.King@census.gov

Presenter: Ruth E. Detlefsen Ruth.E.Detlefsen@census.gov