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The Survey of Labour and Income Dynamics (SLID), introduced in the 1993 reference year, is a longitudinal panel survey of individuals. The purpose of the survey is to measure changes in the economic well-being of individuals and the factors that influence these changes. SLID's sample is divided into two overlapping panels, each six years in length. Longitudinal surveys like SLID are complex due to the dynamic nature of the sample, which in turn is due to the ever-changing composition of households and families over the years. For each reference year, SLID produces two sets of weights: one is representative of the initial population (the longitudinal weights), while the other is representative of the current population (the cross-sectional weights). Since 2002, SLID has been producing a third set of weights which combines two panels that overlap to form a new longitudinal sample. The new weights are referred to as combined longitudinal weights.

For the production of the cross-sectional weights, SLID combines two independent samples and assigns a probability of selection to individuals who joined the sample after the panel was selected. Like cross-sectional weights, longitudinal weights are adjusted for non-response and influential values. In addition, the sample is adjusted to make it representative of the target population. The purpose of this document is to describe SLID's methodology for the longitudinal and cross-sectional weights, as well as to present problems that have been encountered, and solutions that have been proposed. For the purpose of illustration, results for the 2003 reference year are used. The methodology used to produce the combined longitudinal weights will not be presented in this document as there is a complete description in Naud (2004).

This guide will be of assistance when using the public use microdata file (PUMF) of the Survey of Financial Security (SFS) conducted by the Pensions and Wealth Surveys Section of the Income Statistics Division.

The concept of 'nearest proportional to size sampling designs' originated by Gabler (1987) is used to obtain an optimal controlled sampling design, ensuring zero selection probabilities to non-preferred samples. Variance estimation for the proposed optimal controlled sampling design using the Yates-Grundy form of the Horvitz-Thompson estimator is discussed. The true sampling variance of the proposed procedure is compared with that of the existing optimal controlled and uncontrolled high entropy selection procedures. The utility of the proposed procedure is demonstrated with the help of examples.

This publication presents a comprehensive overview of the Canadian trucking industry, both for-hire and private (own account). Principal information includes statistics on revenues and expenses, equipment operated, investment, employment, and commodities transported from point of origin to point of destination. Also included are special studies, a glossary and an explanation of data quality measures and methodology.

The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes.

Sample surveys are subject to errors. As with all surveys conducted at Statistics Canada, considerable time and effort is taken to control such errors at every stage of the Survey of Labour and Income Dynamics. Nonetheless errors do occur. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes a set of quality measures that has been produced in an attempt to describe the overall quality of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.

Users will analyse and interpret the time series of estimates in various ways often involving estimates for several time periods. Despite the large sample sizes and degree of overlap between the sample for some periods the sampling errors can still substantially affect the estimates of movements and functions of them used to interpret the series of estimates. We consider how to account for sampling errors in the interpretation of the estimates from repeated surveys and how to inform the users and analysts of their possible impact.

We propose a generalization of the usual coefficient of variation (CV) to address some of the known problems when used in measuring quality of estimates. Some of the problems associated with CV include interpretation when the estimate is near zero, and the inconsistency in the interpretation about precision when computed for different one-to-one monotonic transformations.

A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. It will be shown by a MonteCarlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others.

The Survey on Employment, Payrolls and Hours is a monthly survey using two data sources: a census of administrative records and an establishment survey. The survey data is used to build models in order to mass impute several derived variables on the administrative source. The survey design relies on the fact that the concepts for number of employees and gross monthly payroll are the same on the two data sources. In this presentation, we will describe different solutions that were brought to the survey design and to the mass imputation model to allow us to get around this conceptual difference, hence producing estimates that are more stable in time. Results from different estimation scenarios for average weekly earnings will be given to conclude the presentation.

This publication presents a comprehensive overview of the Canadian trucking industry, both for-hire and private (own account). Principal information includes statistics on revenues and expenses, equipment operated, investment, employment, and commodities transported from point of origin to point of destination. Also included are special studies, a glossary and an explanation of data quality measures and methodology.

The Survey of Labour and Income Dynamics (SLID), introduced in the 1993 reference year, is a longitudinal panel survey of individuals. The purpose of the survey is to measure changes in the economic well-being of individuals and the factors that influence these changes. SLID's sample is divided into two overlapping panels, each six years in length. Longitudinal surveys like SLID are complex due to the dynamic nature of the sample, which in turn is due to the ever-changing composition of households and families over the years. For each reference year, SLID produces two sets of weights: one is representative of the initial population (the longitudinal weights), while the other is representative of the current population (the cross-sectional weights). Since 2002, SLID has been producing a third set of weights which combines two panels that overlap to form a new longitudinal sample. The new weights are referred to as combined longitudinal weights.

For the production of the cross-sectional weights, SLID combines two independent samples and assigns a probability of selection to individuals who joined the sample after the panel was selected. Like cross-sectional weights, longitudinal weights are adjusted for non-response and influential values. In addition, the sample is adjusted to make it representative of the target population. The purpose of this document is to describe SLID's methodology for the longitudinal and cross-sectional weights, as well as to present problems that have been encountered, and solutions that have been proposed. For the purpose of illustration, results for the 2003 reference year are used. The methodology used to produce the combined longitudinal weights will not be presented in this document as there is a complete description in Naud (2004).

The concept of 'nearest proportional to size sampling designs' originated by Gabler (1987) is used to obtain an optimal controlled sampling design, ensuring zero selection probabilities to non-preferred samples. Variance estimation for the proposed optimal controlled sampling design using the Yates-Grundy form of the Horvitz-Thompson estimator is discussed. The true sampling variance of the proposed procedure is compared with that of the existing optimal controlled and uncontrolled high entropy selection procedures. The utility of the proposed procedure is demonstrated with the help of examples.

The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes.

Sample surveys are subject to errors. As with all surveys conducted at Statistics Canada, considerable time and effort is taken to control such errors at every stage of the Survey of Labour and Income Dynamics. Nonetheless errors do occur. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes a set of quality measures that has been produced in an attempt to describe the overall quality of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.

Users will analyse and interpret the time series of estimates in various ways often involving estimates for several time periods. Despite the large sample sizes and degree of overlap between the sample for some periods the sampling errors can still substantially affect the estimates of movements and functions of them used to interpret the series of estimates. We consider how to account for sampling errors in the interpretation of the estimates from repeated surveys and how to inform the users and analysts of their possible impact.

We propose a generalization of the usual coefficient of variation (CV) to address some of the known problems when used in measuring quality of estimates. Some of the problems associated with CV include interpretation when the estimate is near zero, and the inconsistency in the interpretation about precision when computed for different one-to-one monotonic transformations.

A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. It will be shown by a MonteCarlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others.

The Survey on Employment, Payrolls and Hours is a monthly survey using two data sources: a census of administrative records and an establishment survey. The survey data is used to build models in order to mass impute several derived variables on the administrative source. The survey design relies on the fact that the concepts for number of employees and gross monthly payroll are the same on the two data sources. In this presentation, we will describe different solutions that were brought to the survey design and to the mass imputation model to allow us to get around this conceptual difference, hence producing estimates that are more stable in time. Results from different estimation scenarios for average weekly earnings will be given to conclude the presentation.

This guide will be of assistance when using the public use microdata file (PUMF) of the Survey of Financial Security (SFS) conducted by the Pensions and Wealth Surveys Section of the Income Statistics Division.