Survey of Electronic Commerce and Technology: Past, Present and Future Challenges

Jason Raymond

Third International Conference on Establishment Surveys

June 2007

Outline

Description of the survey

Methodology

Improvements to the sample design

Weighted Outliers

Future challenges

Description of the survey

Annual survey in place since 1999

Cross-economy surveySome exceptions at sub-industry level

Domains of interest:NAICS, SIZE (number of employees)

Description of the survey

Two-page questionnaire with questions on:Use of information and communications technologies (Internet, intranet, web site, …)Use of electronic commerce for the purchase and sale of goods and servicesBarriers to electronic commerce

Types of questions: Mostly categoricalSome numerical

total sales over Internetpercentages

Methodology

SamplingUniverse

Statistics Canada’s Business Register List of public units

Target populationFixed thresholds of exclusion:

$100,000 or $250,000 in gross business income depending on industryCovers approximately 95% of income in each industry

around 700,000 businesses

Methodology

SamplingStratification

NAICS3, NAICS4

Size:0 to 19 employees

20 to 99 employees

100 to 499 employees

500 employees and more -> Take-all stratum

Public/private sector

Take-some strata

Methodology

SamplingNeyman allocation

Sample SelectionSample size: around 19,000 enterprises

Maximum overlap between two consecutive years:Kish and Scott method (1971)

Approximately 70% overlap

Outlier detectionVariables:

Sales over Internet

Year over year difference for sales over Internet

Method: Variant of sigma gap

Distance measure between observations

Methodology

Partial nonresponse (8.3%) imputationDeductive (1%)

Historical (0.1%)

Administrative (0.02%)

Donor (7.2%)

Total nonresponse (31%) reweighting

Methodology

Methodology

Estimation using Statistics Canada’s Generalized Estimation System (GES)

Types of estimatesMeans

Totals

Proportions

Ratios

Data quality measures based on CVs and imputation rates

Improvements to the sample design

When?Current sample design tested in 2004 in parallel with original design and adopted in 2005

Why?Improve the comparability of estimates over time

Need for estimates by size of enterprise

Target populationOriginal sampling design:

Units accounting for 95% of the total income

Drawback: Unstable population over time

New sampling designFixed thresholds of exclusion: $100,000 or $250,000 depending on the industry

Improvements to the sample design

Stratification and allocationOriginal sampling design

NAICS3, NAICS4

Lavallée-Hidiroglou: 2 take-some strata and 1 take-all stratum

Auxiliary variable: GROSS BUSINESS INCOME

Drawback: Not efficient for estimates by size (Number of employees)

Improvements to the sample design

New sampling designStratification:

NAICS3, NAICS4

Size:0 to 19 employees

20 to 99 employees

100 to 499 employees

500 employees and more -> Take-all stratum

Public/private

Neyman allocation

Improvements to the sample design

Take-some strata

Weighted Outliers

Small proportions of firms sell over Internet (8% of private sector and 16% public sector)

Moderate values but large weights sometimes significantly influence estimates

Previously outlier detection uniquely for unweighted values of sales over the Internet

Weighted Outliers

Weighted outlier detection and treatment implemented in 2006Same detection method as for unweighted values (variant of sigma gap method)Treatment methods studied

Hidiroglou/Srinath WinsorizationDalén and Tambay Promotion to own stratum

Hidiroglou/Srinath (1981)Weight reduction method

Minimizes MSE of estimator for total

Requires use of population characteristics which are unknown, and which may possibly not be estimated reliably.

Weighted Outliers

WinsorizationReduces values larger than a certain cutoff to the cutoff itself (dependent on outlier detection method)

Modified to weight reduction method

Weighted Outliers

Dalén(1987) and Tambay(1988)Cross between Winsorization and weight reduction The cutoff for weighted outlier detection is determined for each stratumOutlier value is split into two parts:

Portion less than the cutoff which receives the same new weight as the non-outliers;Portion greater than the cutoff which is allocated a weight of 1

Weighted Outliers

Weighted Outliers

Promotion to own stratumOutliers assigned a weight of 1

Remaining units in stratum have their weights adjusted

Outlier represents only itself during estimation

Implemented method: Dalén and TambayFewer assumptions

Nice compromiseImpact on the estimates is reduced

Not as drastic as promotion to own stratum

Method performed well using 2005 data

Additional empirical studies to confirm effectiveness of the method (simulations?)

Weighted Outliers

Future challenges

Response burdenMaximising overlap = increased response burden?

Minimal effect on response rates

Conditioning effect?

Sample rotation:Ease response burden

Control sample overlap for longitudinal analysis

Statistics Canada’s Business Register redesign

Sampling elements based on operating structure VS statistical structure

Certain modeled variables replaced by administrative data

Future challenges

Pour plus d’information, veuillez contacter

For more information please contact

www.statcan.ca

Jason Raymond613-951-1917

Jason.Raymond@statcan.ca