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The migration data are released annually from a modelled databank that monitors and tracks the movements of people to and within Canada. Data are derived from the comparison of two consecutive years of tax files. Most current data show migration between 2011 and 2012.

Data on international migration and internal migration are generally available 15 to 18 months after the tax filing deadline.

The data consist of estimates of migration flows between census divisions (CDs) or census metropolitan areas (CMAs), by sex and broad age groups. Information may be requested by province of origin/destination, by age group, by type of migration (interprovincial, intraprovincial and international) and sex; and for migration flows by census division of origin/destination, or by census metropolitan area/non-census metropolitan area of origin/destination. The statistics are derived from the annual tax file provided by the Canada Revenue Agency.

Note: Intraprovincial migration: movement of people between two CDs or CMAs located within the same province. The CD/CMA of departure is the CD/CMA of origin and the CD/CMA of arrival is the CD/CMA of destination. Interprovincial migration: movement of people between CDs and CMAs located in two different provinces. The province of departure is the province of origin and the province of arrival is the province of destination. International migration: movement of people between an area in Canada and another country. Migration flows: migration flows for any given CD or CMA. The flows are listed in descending order of net migration for the most recent year of migration.

Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.

In most surveys all sample units receive the same treatment and the same design features apply to all selected people and households. In this paper, it is explained how survey designs may be tailored to optimize quality given constraints on costs. Such designs are called adaptive survey designs. The basic ingredients of such designs are introduced, discussed and illustrated with various examples.

A considerable limitation of current methods for automatic data editing is that they treat all edits as hard constraints. That is to say, an edit failure is always attributed to an error in the data. In manual editing, however, subject-matter specialists also make extensive use of soft edits, i.e., constraints that identify (combinations of) values that are suspicious but not necessarily incorrect. The inability of automatic editing methods to handle soft edits partly explains why in practice many differences are found between manually edited and automatically edited data. The object of this article is to present a new formulation of the error localisation problem which can distinguish between hard and soft edits. Moreover, it is shown how this problem may be solved by an extension of the error localisation algorithm of De Waal and Quere (2003).

SILC (Statistics on Income and Living Conditions) is an annual European survey that measures the population's income distribution, poverty and living conditions. It has been conducted in Switzerland since 2007, based on a four-panel rotation scheme that yields both cross-sectional and longitudinal estimates. This article examines the problem of estimating the variance of the cross-sectional poverty and social exclusion indicators selected by Eurostat. Our calculations take into account the non-linearity of the estimators, total non-response at different survey stages, indirect sampling and calibration. We adapt the method proposed by Lavallée (2002) for estimating variance in cases of non-response after weight sharing, and we obtain a variance estimator that is asymptotically unbiased and very easy to program.

A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization framework for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation.

We consider two different self-benchmarking methods for the estimation of small area means based on the Fay-Herriot (FH) area level model: the method of You and Rao (2002) applied to the FH model and the method of Wang, Fuller and Qu (2008) based on augmented models. We derive an estimator of the mean squared prediction error (MSPE) of the You-Rao (YR) estimator of a small area mean that, under the true model, is correct to second-order terms. We report the results of a simulation study on the relative bias of the MSPE estimator of the YR estimator and the MSPE estimator of the Wang, Fuller and Qu (WFQ) estimator obtained under an augmented model. We also study the MSPE and the estimators of MSPE for the YR and WFQ estimators obtained under a misspecified model.

We consider conservative variance estimation for the Horvitz-Thompson estimator of a population total in sampling designs with zero pairwise inclusion probabilities, known as "non-measurable" designs. We decompose the standard Horvitz-Thompson variance estimator under such designs and characterize the bias precisely. We develop a bias correction that is guaranteed to be weakly conservative (nonnegatively biased) regardless of the nature of the non-measurability. The analysis sheds light on conditions under which the standard Horvitz-Thompson variance estimator performs well despite non-measurability and where the conservative bias correction may outperform commonly-used approximations.

Readers will find a complete analysis of factors affecting the comparability of Language results between the censuses in the Methodological Document on the 2011 Census Language Data.

Data matching is a common practice used to reduce the response burden of respondents and to improve the quality of the information collected from respondents when the linkage method does not introduce bias. However, historical linkage, which consists in linking external records from previous years to the year of the initial wave of a survey, is relatively rare and, until now, had not been used at Statistics Canada. The present paper describes the method used to link the records from the Living in Canada Survey pilot to historical tax data on income and labour (T1 and T4 files). It presents the evolution of the linkage rate going back over time and compares earnings data collected from personal income tax returns with those collected from employers file. To illustrate the new possibilities of analysis offered by this type of linkage, the study concludes with an earnings profile by age and sex for different cohorts based on year of birth.

This package was designed to help users access and manipulate the public use microdata file (PUMF – provincial) for the 2014 General Social Survey (GSS) on Canadians’ Safety (Victimization). It contains the PUMF data and describes the objectives, methodology and estimation procedures for this survey as well as guidelines for releasing estimates.

Statistics Canada collected data on the topic of Canadians’ safety (Victimization) for the sixth time in 2014. Data were previously collected in 1988 (Cycle 3), 1993 (Cycle 8), 1999 (Cycle 13), 2004 (Cycle 18) and 2009 (Cycle 23). The 2014 provincial GSS collected data from persons aged 15 years and over living in private households in Canada, excluding residents of the Yukon, Northwest Territories and Nunavut and full time residents of institutions.

Between 2009 and 2014, the core content of the survey was revised in a number of ways, based on experience gained from earlier iterations. Some questions were revised to improve their clarity for respondents, and others were added or dropped following consultations with stakeholders from the justice sector, government and academic communities.

This package was designed to enable users to access and manipulate the microdata file for Cycle 23 (2009) of the General Social Survey (GSS). It contains information on the objectives, methodology and estimation procedures, as well as guidelines for releasing estimates based on the survey.

Cycle 23 collected data from persons 15 years and over living in private households in Canada, excluding residents of the Yukon, Northwest Territories and Nunavut; and full-time residents of institutions.

The purpose of this survey is to better understand how Canadians perceive crime and the justice system and their experiences of victimization. The survey is designed to produce estimates of the extent to which persons are the victims of eight types of offences (assault, sexual assault, robbery, theft of personal property, breaking and entering, motor vehicle theft, theft of household property and vandalism); to examine the risk factors associated with victimization; to examine the rates of reporting to the police; and to evaluate the fear of crime and public perceptions of crime and the criminal justice system.

Cycle 23 is the fifth cycle of the GSS dedicated to collecting data on victimization. Previous cycles had been conducted in 1988, 1993, 1999 and 2004. Cycle 23 includes most of the content from previous cycles as well as new content, added to reflect the society's emerging issues of crime prevention and Internet victimization.

This package was designed to enable users to access and manipulate the microdata file for the 21st cycle (2007) of the General Social Survey (GSS). It contains information on the objectives, methodology and estimation procedures, as well as guidelines for releasing estimates based on the survey. Cycle 21 of the GSS collected data from persons aged 45 years and over living in private households in the 10 provinces of Canada. The survey covered a wide range of topics such as well-being, family composition, retirement decisions and plans, care giving and care receiving experiences, social networks and housing.

Cycle 15 of the General Social Survey (GSS) is the third cycle to collect detailed information on family life in Canada. The previous GSS cycles that collected family data were Cycles 5 and 10. Topics include demographic characteristics such as age, sex, and marital status; family origin of parents; brothers and sisters; marriages of respondent; common-law unions of respondent; fertility and family intentions; values and attitudes; education history; work history; main activity and other characteristics.

The target population for Cycle 15 of the GSS is all persons 15 years of age and older in Canada, excluding residents of the Yukon, Northwest Territories, and Nunavut, and full-time residents of institutions.

This publication presents data on young offender admissions to custody and community services, with breakdowns by custody (remand, secure, open) and probation, and key case characteristics, such as age, sex, Aboriginal/non-Aboriginal status and most serious offence. In addition, it includes data pertaining to releases from remand, secure custody and open custody, by sex and time served. These breakdowns are presented at the national and provincial/territorial levels.

Data presented in this publication are drawn from two primary sources: 1) The Youth Custody and Community Services (YCCS) Survey. The objective of this survey is to collect and analyse information on the application of dispositions under the Young Offenders Act from provincial and territorial agencies responsible for youth corrections and programs. 2) The Youth Key Indicator Report (YKIR). This survey measures the average counts of youth in custody (remand, secure and open) and on probation. The YKIR describes average daily counts (caseload), which measure the volume of offenders held in custody or on probation on an average day at month-end. This information also provides an examination of youth incarceration and probation rates in Canada.

This Statistics Canada publication is a collection of five annotated maps and graphs that describe the geographic distribution of manure in Canada by river basin. The amount of manure produced is estimated along with some of the major substances found in manure: (i) nitrogen, (ii) phosphorus, (iii) total coliform bacteria and (iv) fecal coliform bacteria.

The maps and figures presented in this report indicate that there are geographic areas in Canada characterized by higher levels of total livestock manure and related production of nitrogen, phosphorus and bacteria. Areas that repeatedly showed the highest levels were found in central and southern Alberta, southern Manitoba, southern Ontario, southeastern Quebec, parts of Prince Edward Island, the west Fraser River area in southern British Columbia and an area near Wolfville and Kentville, Nova Scotia.

This publication presents data on revenue and expense items from wholesalers and retailers by trade group and by province. There are 16 retail trade groups and 11 wholesale trade groups. Profiles of the retail trade groups are provided for each individual province/territory and are comprised of: number of locations, total operating revenues, cost of goods sold, gross margin and employee earnings and benefits. At the national level for each retail trade group are the following performance indicators: sales to inventory, cost of goods sold to inventory, gross margin and profit margin. Also provided at the national level are total operating revenues, number of locations for independent and for chain stores, the number of retail businesses by trade and total operating revenue by SIC (4 digit when possible).

The 11 wholesale trade groups, plus grain and petroleum products, are profiled for each individual province/territory giving: number of locations, total operating revenues, cost of goods sold, gross margin and employee earnings and benefits. Performance indicators by trade group at the national level including sales to inventory, cost of goods sold to inventory, gross margin and profit margin are provided. Total operating revenue at the national level by Standard Industrial Classification and by class of customer and trade group are also provided.

This publication presents information on the income, expenditure and assets of all trusteed pension funds in Canada in both the public and private sectors. Data are presented at the Canada level. The publication contains an analysis of the funds based on the size of the fund, the number of members and the type of benefit. It is a continuation of a series of reports produced since 1957. As a single pool of investment capital in Canada, these funds are surpassed in size only by the aggregate reserves held by the chartered banks.

With this working paper, Statistics Canada is releasing 1991 Census data tabulated by a new geographic classification called "census metropolitan area and census agglomeration influenced zones", or MIZ. This classification applies to census subdivisions (municipalities) that lie outside census metropolitan areas and census agglomerations. This part of Canada covers 96% of the country's total land mass and contains 22% of its population, yet up to now we have been limited in our means of differentiating this vast area. The MIZ classification shows the influence of census metropolitan areas (CMA) and census agglomerations (CA) on surrounding census subdivisions as measured by commuting flows based on 1991 Census place of work data. This version of the MIZ classification also incorporates a preliminary version of a north concept that flags census subdivisions according to their location in the north or south of Canada.

The series of tables presented here show detailed demographic, social and economic characteristics for Canada as a whole, for the six major regions of Canada, and for individual provinces and territories. Within each table, the data are subdivided into five categories: census metropolitan area or census agglomeration, strong MIZ, moderate MIZ, weak MIZ and no MIZ. Within each of these categories, the data are further subdivided into north and south.

Readers are invited to review and use the data tables to assess whether this combined MIZ and north/south classification of non-CMA/CA areas provides sufficient detail to support data analysis and research. The intent of this MIZ classification is to reveal previously hidden data detail and thereby help users address issues related to this vast geographic area.

This is the first of three related Geography working papers (catalogue no. 92F0138MPE). The second working paper (no. 2000-2, 92F0138MPE00002) provides background information about the methodology used to delineate the MIZ classification. The third working paper (no. 2000-3, 92F0138MPE00003) describes the methodology used to define a continuous line across Canada that separates the north from the south to further differentiate the MIZ classification.

Consulting Engineering Services Price Index (CEPI) is an annual index that measures changes in the prices for services provided by consulting engineers. These services encompass advisory and design work as well as construction or project management. They are provided for many types of projects (fields of specialization), and to both Canadian and foreign clients. Price indexes are published for 10 fields of specialization as well as for national, regional, and foreign markets.

This paper introduces a R-package for the stratification of a survey population using a univariate stratification variable X and for the calculation of stratum sample sizes. Non iterative methods such as the cumulative root frequency method and the geometric stratum boundaries are implemented. Optimal designs, with stratum boundaries that minimize either the CV of the simple expansion estimator for a fixed sample size n or the n value for a fixed CV can be constructed. Two iterative algorithms are available to find the optimal stratum boundaries. The design can feature a user defined certainty stratum where all the units are sampled. Take-all and take-none strata can be included in the stratified design as they might lead to smaller sample sizes. The sample size calculations are based on the anticipated moments of the survey variable Y, given the stratification variable X. The package handles conditional distributions of Y given X that are either a heteroscedastic linear model, or a log-linear model. Stratum specific non-response can be accounted for in the design construction and in the sample size calculations.

In two-phase sampling for stratification, the second-phase sample is selected by a stratified sample based on the information observed in the first-phase sample. We develop a replication-based bias adjusted variance estimator that extends the method of Kim, Navarro and Fuller (2006). The proposed method is also applicable when the first-phase sampling rate is not negligible and when second-phase sample selection is unequal probability Poisson sampling within each stratum. The proposed method can be extended to variance estimation for two-phase regression estimators. Results from a limited simulation study are presented.

We analyze the statistical and economic efficiency of different designs of cluster surveys collected in two consecutive time periods, or waves. In an independent design, two cluster samples in two waves are taken independently from one another. In a cluster-panel design, the same clusters are used in both waves, but samples within clusters are taken independently in two time periods. In an observation-panel design, both clusters and observations are retained from one wave of data collection to another. By assuming a simple population structure, we derive design variances and costs of the surveys conducted according to these designs. We first consider a situation in which the interest lies in estimation of the change in the population mean between two time periods, and derive the optimal sample allocations for the three designs of interest. We then propose the utility maximization framework borrowed from microeconomics to illustrate a possible approach to the choice of the design that strives to optimize several variances simultaneously. Incorporating the contemporaneous means and their variances tends to shift the preferences from observation-panel towards simpler panel-cluster and independent designs if the panel mode of data collection is too expensive. We present numeric illustrations demonstrating how a survey designer may want to choose the efficient design given the population parameters and data collection cost.

This paper examines the efficiency of the Horvitz-Thompson estimator from a systematic probability proportional to size (PPS) sample drawn from a randomly ordered list. In particular, the efficiency is compared with that of an ordinary ratio estimator. The theoretical results are confirmed empirically with of a simulation study using Dutch data from the Producer Price Index.

In the calibration method proposed by Deville and Särndal (1992), the calibration equations take only exact estimates of auxiliary variable totals into account. This article examines other parameters besides totals for calibration. Parameters that are considered complex include the ratio, median or variance of auxiliary variables.

In order to provide a holographic or complete picture of low income, Statistics Canada uses three complementary low income lines: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs) and the Market Basket Measure (MBM). While the first two lines were developed by Statistics Canada, the MBM is based on concepts developed by Human Resources and Skill Development Canada. Though these measures differ from one another, they give a generally consistent picture of low income status over time. None of these measures is the best. Each contributes its own perspective and its own strengths to the study of low income, so that cumulatively, the three provide a better understanding of the phenomenon of low income as a whole. These measures are not measures of poverty, but strictly measures of low income.

The purpose of this paper is twofold. First, the authors provide a detailed social accounting matrix (SAM), which incorporates the income and financial flows into the standard input-output matrix, for the Canadian economy for 2004. Second, they use the SAM to assess the strength of the real-financial linkages by calculating and comparing real SAM multipliers and financial social accounting matrix (FSAM) multipliers. For FSAM multipliers, financial flows are endogenous, whereas for real SAM multipliers they are not. The results show that taking into account financial flows increases the impact of a final demand shock on Canadian output. Financial flows also play an important role in determining the cumulative effect of an income shock or the availability of investment funds. Between 2008 and the first half of 2009, financial institutions shifted their investments toward government bonds, short-term paper, and foreign investments. This shift together with the fact that non-financial institutions were unwilling or unable to increase their financial liabilities, led to estimated declines in all GDP multipliers between 2008 and the first half of 2009 (2009H1). The main advantage of using the extended input-output analysis is that it provides a simple framework, with very few assumptions, which allows the assessment of the strength of real-financial linkages by means of multipliers. However, the methodology is subject to the Lucas critique, that as shocks shift prices, agents cannot adjust. Such a framework is, nevertheless, appropriate in short-term impact analysis such as this study.

The paper explores and assesses the approaches used by statistical offices to ensure effective methodological input into their statistical practice. The tension between independence and relevance is a common theme: generally, methodologists have to work closely with the rest of the statistical organisation for their work to be relevant; but they also need to have a degree of independence to question the use of existing methods and to lead the introduction of new ones where needed. And, of course, there is a need for an effective research program which, on the one hand, has a degree of independence needed by any research program, but which, on the other hand, is sufficiently connected so that its work is both motivated by and feeds back into the daily work of the statistical office. The paper explores alternative modalities of organisation; leadership; planning and funding; the role of project teams; career development; external advisory committees; interaction with the academic community; and research.

One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.

This paper considers the problem of selecting nonparametric models for small area estimation, which recently have received much attention. We develop a procedure based on the idea of fence method (Jiang, Rao, Gu and Nguyen 2008) for selecting the mean function for the small areas from a class of approximating splines. Simulation results show impressive performance of the new procedure even when the number of small areas is fairly small. The method is applied to a hospital graft failure dataset for selecting a nonparametric Fay-Herriot type model.

In this paper, we present the methodology developed by Statistics Canada to calculate the average annual water yield for Canada. Water yield, for the purposes of this paper, is defined as the amount of freshwater derived from unregulated flow (m³ s^-1) measurements for a given geographic area over a defined period of time. The methodology is applied to the 1971 to 2000 time period.

This research was conducted to fill data gaps in Statistics Canada's water statistics program. These gaps exist because estimates of freshwater flow for Canada have not been calculated regularly and have been produced using a variety of methods that do not necessarily generate comparable results. The methodology developed in this study produced results that are coherent through space and time. These results will be used in the future to investigate changes in water yield on a more disaggregated basis.

To achieve the water yield estimate a database of natural streamflow observations from 1971 to 2000 was compiled. The streamflow values were then converted to a runoff depth and interpolated using ordinary kriging to produce spatial estimates of runoff. The spatial estimates were then scaled to create a National estimate of water yield as a thirty-year average. The methodology and results were then validated using a stability analysis and several techniques involving uncertainty. The result of the methodology indicates that the thirty-year average water yield for Canada is 3435 km³.

Since 2007/2008, Statistics Canada has centred analysis of data holdings related to health as well as our program of dissemination of health research within the new Health Information and Research Division (HIRD).

The new division has launched a comprehensive approach to analytical planning including environmental scanning and consultation; establishment of strategic multi-year priorities for health research at Statistics Canada; a process of project selection and review that ensures that analytical effort addresses our priorities; metrics to measure our adherence to priorities and the impact of our analytical effort; and communication and dissemination of analytical plans.

This multi-year analytical plan identifies the key high-level priority areas for Statistics Canada's investment in health research for 2008/2009 to 2010/2011, and serves as a blueprint for subsequent operational research planning.

This guide is intended to help data users understand the concepts and methods used in the 2006 Aboriginal Peoples Survey (APS), which was conducted from October 2006 to March 2007.

Technical details on sampling, processing and data quality are included in this guide. Further, the guide explains the relationship between the APS and the 2006 Census and cautions users as to important differences in the data produced from these two sources. Appendix 1 provides a list of communities for which data are available while Appendix 2 contains a glossary of terms that relate to the APS. Answers to some frequently asked questions are provided in Appendix 3. Links to the 2006 APS questionnaires are found in Appendix 4.

The objective of this study is to examine the feasibility of using provincial and territorial health care files of new registrants as an independent measure of preliminary inter-provincial and inter-territorial migration. The study aims at measuring the conceptual and quantifiable differences between this data source and our present source of the Canada Revenue Agency's Canadian Child Tax Benefit.

Criteria were established to assess the quality and appropriateness of these provincial/territorial health care records as a proxy for our migration estimates: coverage, consistency, timeliness, reliability, level of detail, uniformity and accuracy.

Based on the present analysis, the paper finds that these data do not ameliorate the estimates and would not be suitable at this time as a measure of inter-provincial/territorial migration. These Medicare data though are an important independent data source that can be used for quality evaluation.

This book provides technical documentation of variables, methodologies and extended lists of references used in developing the research findings reported in "New Frontiers of Research on Retirement". It will be used around the world by researchers and teachers, as well as by students preparing theses related to patterns of transition to retirement. This documentation is important because a large part of book is devoted to scientific papers that are based upon Statistics Canada's data and which require substantial innovations of useful concepts and data.

The Canadian Productivity Accounts (CPA) of Statistics Canada maintain two multifactor productivity (MFP) programs.

The Major Sector Multifactor Productivity Program develops the indexes of MFP for the total business sector and major industry groups in the business sector.

The Industry Multifactor Productivity Program or the Industry KLEMS Productivity Program develops the industry productivity database that includes MFP indexes, output, capital (K), labour (L), energy (E), materials (M) and services (S) inputs for the individual industries of the business sector at various levels of industry aggregation. This paper describes the methodologies and data sources that are used to construct the major sector MFP indexes and the industry productivity database (or the KLEMS database). More specifically, this paper is meant to:provide a background of the major sector MFP program and the industry KLEMS productivity program;present the methodology for measuring MFP;describe the data sources and data available from the MFP programs;present a quality rating of the industry KLEMS productivity data; anddescribe the research agenda related to the MFP program.

This guide has been created especially for users needing a step-by-step review on how to find, read and use data, with quick tips on locating information on the Statistics Canada website. Originally published in paper format in the 1980s, revised as part of the 1994 Statistics Canada Catalogue, and then transformed into an electronic version, this guide is continually being updated to maintain its currency and usefulness.

This paper examines the various products associated with the quarterly labour productivity program. It outlines the nature of the volatility in the very short-run estimates and examines properties of the revisions made to the estimates of Canadian labour productivity and its components (gross domestic product and hours worked) since the inception of the program in 2001.

This reference document presents an overview of the different questions used by Statistics Canada to identify Aboriginal peoples. It is divided into three parts. Part one is a brief description of the data sources and their limitations. Part 2 deals with the 2006 census questions used to identify Aboriginal peoples while Part 3 deals with the identification questions used in the Aboriginal Peoples Survey (APS) and the Aboriginal Children's Survey (ACS).

The National Tourism Indicators will be revised back to 2001 and their volume and price estimates converted to a 2002 reference year, effective June 29, 2007.