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The Whole Farm Data Base (WFDB) is the product of a joint venture between Agriculture and Agri-Food Canada and Statistics Canada. It was developed with the intention to meet the increasing demands of users of agricultural statistics for more disaggregated data at the whole farm level. The WFDB provides the means for users to evaluate agricultural policies and programs as well as analyze the viability, stability, and competitiveness of various farm businesses.

Integrating into one base the agricultural data, which are available from administrative and survey sources, is the essence of the WFDB. It can offer users access to a wider than ever range of disaggregated physical and financial data at the farm level.

This reference manual will familiarize potential users of the WFDB with the structure of the data base and the quality of the data and give a description of the WFDB products and services that are currently available.

This working paper profiles Canadian firms involved in the development and production of Bioproducts. It provides data on the number and types of Bioproducts firms in 2009, covering bioproducts revenues, research and development, use of biomass, patents, products, business practices and the impact of government regulations on the sector.

Statistics Canada is engaged in the "Information System for Science and Technology Project" to develop useful indicators of activity and a framework to tie them together into a coherent picture of science and technology (S&T) in Canada. The working papers series is used to publish results of the different initiatives conducted within this project. The data are related to the activities, linkages and outcomes of S&T. Several key areas are covered such as: innovation, technology diffusion, human resources in S&T and interrelations between different actors involved in S&T. This series also presents data tabulations taken from regular surveys on research and development (R&D) and S&T and made possible by the project.

Statistics Canada administers six surveys per year to collect information on intended, seeded and harvested acreages, yields, production and stocks of principal field crops, and publishes these survey estimates in the Field Crop Reporting Series (FCRS). This paper analyses short-term movements in weekly crop prices from the week before the releases of FCRS to the week after the releases. Field crops included in this study are oats, canola, corn, flax, barley and wheat, while specialty crops studied are sunflower seed, canary seed, field peas, lentils, mustard seed, chick peas and green peas. The data for field crops cover a period from 1990 to 2009 and that for specialty crops cover varying periods from 1992 to 2009 based on their availability. The results reveal that the price changes before and after the official releases of FCRS tend to even out over time. The results also suggest that prices after the releases are as likely to increase as they are to decrease. Based on the findings, the study concludes that the publication of statistics in the FCRS has no systematic effect on crop prices. The results are consistent with the findings of the National Agricultural Statistics Service of the United States Department of Agriculture.

This report summarizes annual findings of travel to and from Canada in the form of tables, charts, maps and an analytical review. The publication provides a profile of international travellers by province/country of residence, area of destination, mode of transportation, purpose, length of stay, expenditures, age group and sex.

This article attempts to answer the three questions appearing in the title. It starts by discussing unique features of complex survey data not shared by other data sets, which require special attention but suggest a large variety of diverse inference procedures. Next a large number of different approaches proposed in the literature for handling these features are reviewed with discussion on their merits and limitations. The approaches differ in the conditions underlying their use, additional data required for their application, goodness of fit testing, the inference objectives that they accommodate, statistical efficiency, computational demands, and the skills required from analysts fitting the model. The last part of the paper presents simulation results, which compare the approaches when estimating linear regression coefficients from a stratified sample in terms of bias, variance, and coverage rates. It concludes with a short discussion of pending issues.

In many sample surveys there are items requesting binary response (e.g., obese, not obese) from a number of small areas. Inference is required about the probability for a positive response (e.g., obese) in each area, the probability being the same for all individuals in each area and different across areas. Because of the sparseness of the data within areas, direct estimators are not reliable, and there is a need to use data from other areas to improve inference for a specific area. Essentially, a priori the areas are assumed to be similar, and a hierarchical Bayesian model, the standard beta-binomial model, is a natural choice. The innovation is that a practitioner may have much-needed additional prior information about a linear combination of the probabilities. For example, a weighted average of the probabilities is a parameter, and information can be elicited about this parameter, thereby making the Bayesian paradigm appropriate. We have modified the standard beta-binomial model for small areas to incorporate the prior information on the linear combination of the probabilities, which we call a constraint. Thus, there are three cases. The practitioner (a) does not specify a constraint, (b) specifies a constraint and the parameter completely, and (c) specifies a constraint and information which can be used to construct a prior distribution for the parameter. The griddy Gibbs sampler is used to fit the models. To illustrate our method, we use an example on obesity of children in the National Health and Nutrition Examination Survey in which the small areas are formed by crossing school (middle, high), ethnicity (white, black, Mexican) and gender (male, female). We use a simulation study to assess some of the statistical features of our method. We have shown that the gain in precision beyond (a) is in the order with (b) larger than (c).

We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based simulation, with the latter based on two realistic survey data sets containing small area information.

Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

This paper introduces a U.S. Census Bureau special compilation by presenting four other papers of the current issue: three papers from authors Tillé, Lohr and Thompson as well as a discussion paper from Opsomer.

This report summarizes annual findings of travel to and from Canada in the form of tables, charts, maps and an analytical review. The publication provides a profile of international travellers by province/country of residence, area of destination, mode of transportation, purpose, length of stay, expenditures, age group and sex.

This annual on-line bulletin summarizes the production, price and value of honey and maple products, by province. The bulletin is released during the fall of each year.

This publication provides annual information on operational and financial variables related to the scheduled and charter operations of all Canadian-licensed air carriers classified into reporting levels I to III. Operational data (passengers, passenger-kilometres, goods carried, goods tonne-kilometres, etc.) and financial data (income statement-operating revenues, operating expenses, non-operating income (expenses), balance sheet-assets and liabilities, as well as financial performance indicators) are presented. Information on fuel, on employment, by category, and on wages and salaries paid, by province and territory is also provided. This publication also includes data highlights.

This bulletin presents the final set of tables which contain salary information for the year 2009/2010. This information is collected annually under the University and College Academic Staff System and has a reference date of October 1st. Therefore, the data reflect employment in universities as of that date. Each university must authorize Statistics Canada to release their information. However, information for institutions that have less than 100 full-time staff are not included.

This publication provides quarterly information on operational and financial variables related to the scheduled and charter operations of all Canadian-licensed air carriers classified into reporting levels I and II. Operational data (passengers, passenger-kilometres, goods carried, goods tonne-kilometres, etc.) and financial data (operating revenues, operating expenses, employment expenses) are presented. This publication also includes data highlights and some financial performance indicators and productivity measures.

The report provides elementary and secondary school public data at the provincial, territorial and Canada-wide levels for key education statistics, such as enrolment, graduates, finance, and educator. Data is collected for a five-year period which allows for extensive review of the data going back to 1997, the first school year that elementary-secondary education statistics are on file. Private school data for years 2007/2008 to 2009/2010 covers enrolment, graduates and educators.

Detailed and summary data tables for the trade in culture goods, along with cross-tabulations for trade between Canada and selected countries.

Culture products are selected using Statistics Canada's Framework for Culture Statistics. Essentially the framework defines culture products as those that result from creative expression.

Examples of culture goods include books; magazines; newspapers; postcards; calendars; films; videos; digital videodiscs (DVDs); sheet music; compact discs (CDs); cassettes; vinyl long-playing phonograph records (LPs); paintings (original and reproductions); photographs; sculptures; ornaments and figurines; architectural plans, designs and drawings; advertising materials; museum exhibits; coin and stamp collections; and antiques. Unrecorded media, such as blank CDs, are not included.

This publication contains annual data for farm cash receipts for Canada and the provinces from 1926 to date. Data highlights and concepts and methods are also included.

In May, annual measures for the previous two calendar years are subject to revision. In November, estimates for the previous three years may be revised. Every five years a historical revision is done, based on the results of the Census of Agriculture. Although the data are available in late May and late November, the publication is not completed and released until the following July and January, respectively.

This publication contains annual data from 1926 to date for farm operating expenses and depreciation charges for Canada and the provinces. Data highlights and concepts and methods are also included.

In May, annual measures for the previous two calendar years are subject to revision. In November, estimates for the previous three years may be revised. Every five years a historical revision is done, based on the results of the Census of Agriculture. Although the data are available in late May and late November, the publication is not completed and released until the following July and January, respectively.

This publication contains annual data, at July 1, for Canada and the provinces: from 1926 to date for the value of farm capital; from 1921 to date for the value per acre of farm land and buildings; and from 1986 to date for the value per head of livestock and poultry. Data highlights, concepts and methods are also included.

In May, annual measures for the previous two calendar years are subject to revision. In November, estimates for the previous three years may be revised. Every five years a historical revision is done, based on the results of the Census of Agriculture. Although the data are available in late May and late November, the publication is not completed and released until the following July and January, respectively.

Note to readersData in the following tables was changed on December 2, 2008:Tables 1-4, 1-5 and Table 2

This working paper profiles Canadian firms involved in the development and production of Bioproducts. It provides data on the number and types of Bioproducts firms in 2009, covering bioproducts revenues, research and development, use of biomass, patents, products, business practices and the impact of government regulations on the sector.

Statistics Canada is engaged in the "Information System for Science and Technology Project" to develop useful indicators of activity and a framework to tie them together into a coherent picture of science and technology (S&T) in Canada. The working papers series is used to publish results of the different initiatives conducted within this project. The data are related to the activities, linkages and outcomes of S&T. Several key areas are covered such as: innovation, technology diffusion, human resources in S&T and interrelations between different actors involved in S&T. This series also presents data tabulations taken from regular surveys on research and development (R&D) and S&T and made possible by the project.

Statistics Canada administers six surveys per year to collect information on intended, seeded and harvested acreages, yields, production and stocks of principal field crops, and publishes these survey estimates in the Field Crop Reporting Series (FCRS). This paper analyses short-term movements in weekly crop prices from the week before the releases of FCRS to the week after the releases. Field crops included in this study are oats, canola, corn, flax, barley and wheat, while specialty crops studied are sunflower seed, canary seed, field peas, lentils, mustard seed, chick peas and green peas. The data for field crops cover a period from 1990 to 2009 and that for specialty crops cover varying periods from 1992 to 2009 based on their availability. The results reveal that the price changes before and after the official releases of FCRS tend to even out over time. The results also suggest that prices after the releases are as likely to increase as they are to decrease. Based on the findings, the study concludes that the publication of statistics in the FCRS has no systematic effect on crop prices. The results are consistent with the findings of the National Agricultural Statistics Service of the United States Department of Agriculture.

This article attempts to answer the three questions appearing in the title. It starts by discussing unique features of complex survey data not shared by other data sets, which require special attention but suggest a large variety of diverse inference procedures. Next a large number of different approaches proposed in the literature for handling these features are reviewed with discussion on their merits and limitations. The approaches differ in the conditions underlying their use, additional data required for their application, goodness of fit testing, the inference objectives that they accommodate, statistical efficiency, computational demands, and the skills required from analysts fitting the model. The last part of the paper presents simulation results, which compare the approaches when estimating linear regression coefficients from a stratified sample in terms of bias, variance, and coverage rates. It concludes with a short discussion of pending issues.

In many sample surveys there are items requesting binary response (e.g., obese, not obese) from a number of small areas. Inference is required about the probability for a positive response (e.g., obese) in each area, the probability being the same for all individuals in each area and different across areas. Because of the sparseness of the data within areas, direct estimators are not reliable, and there is a need to use data from other areas to improve inference for a specific area. Essentially, a priori the areas are assumed to be similar, and a hierarchical Bayesian model, the standard beta-binomial model, is a natural choice. The innovation is that a practitioner may have much-needed additional prior information about a linear combination of the probabilities. For example, a weighted average of the probabilities is a parameter, and information can be elicited about this parameter, thereby making the Bayesian paradigm appropriate. We have modified the standard beta-binomial model for small areas to incorporate the prior information on the linear combination of the probabilities, which we call a constraint. Thus, there are three cases. The practitioner (a) does not specify a constraint, (b) specifies a constraint and the parameter completely, and (c) specifies a constraint and information which can be used to construct a prior distribution for the parameter. The griddy Gibbs sampler is used to fit the models. To illustrate our method, we use an example on obesity of children in the National Health and Nutrition Examination Survey in which the small areas are formed by crossing school (middle, high), ethnicity (white, black, Mexican) and gender (male, female). We use a simulation study to assess some of the statistical features of our method. We have shown that the gain in precision beyond (a) is in the order with (b) larger than (c).

We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based simulation, with the latter based on two realistic survey data sets containing small area information.

Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

This paper introduces a U.S. Census Bureau special compilation by presenting four other papers of the current issue: three papers from authors Tillé, Lohr and Thompson as well as a discussion paper from Opsomer.

This paper describes recent developments in adaptive sampling strategies and introduces new variations on those strategies. Recent developments described included targeted random walk designs and adaptive web sampling. These designs are particularly suited for sampling in networks; for example, for finding a sample of people from a hidden human population by following social links from sample individuals to find additional members of the hidden population to add to the sample. Each of these designs can also be translated into spatial settings to produce flexible new spatial adaptive strategies for sampling unevenly distributed populations. Variations on these sampling strategies include versions in which the network or spatial links have unequal weights and are followed with unequal probabilities.

Designs and estimators for the single frame surveys currently used by U.S. government agencies were developed in response to practical problems. Federal household surveys now face challenges of decreasing response rates and frame coverage, higher data collection costs, and increasing demand for small area statistics. Multiple frame surveys, in which independent samples are drawn from separate frames, can be used to help meet some of these challenges. Examples include combining a list frame with an area frame or using two frames to sample landline telephone households and cellular telephone households. We review point estimators and weight adjustments that can be used to analyze multiple frame surveys with standard survey software, and summarize construction of replicate weights for variance estimation. Because of their increased complexity, multiple frame surveys face some challenges not found in single frame surveys. We investigate misclassification bias in multiple frame surveys, and propose a method for correcting for this bias when misclassification probabilities are known. Finally, we discuss research that is needed on nonsampling errors with multiple frame surveys.

The Whole Farm Data Base (WFDB) is the product of a joint venture between Agriculture and Agri-Food Canada and Statistics Canada. It was developed with the intention to meet the increasing demands of users of agricultural statistics for more disaggregated data at the whole farm level. The WFDB provides the means for users to evaluate agricultural policies and programs as well as analyze the viability, stability, and competitiveness of various farm businesses.

Integrating into one base the agricultural data, which are available from administrative and survey sources, is the essence of the WFDB. It can offer users access to a wider than ever range of disaggregated physical and financial data at the farm level.

This reference manual will familiarize potential users of the WFDB with the structure of the data base and the quality of the data and give a description of the WFDB products and services that are currently available.

This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research for the Survey of Labour and Income Dynamics in 2009.

The first issue of the series presents the Conceptual Framework for Culture Statistics 2011, a revision of the 2004 Canadian Framework for Culture Statistics.

The conceptual framework contains an official statistical definition of culture and describes a set of culture domains that can be used to measure culture from creation to use.

The second issue of this series is a companion piece to the Conceptual Framework for Culture Statistics 2011, a revision to the 2004 Canadian Framework for Culture Statistics.

The guide maps the 2011 Canadian framework for culture statistics to the following Statistics Canada's standard classification systems: the North American Industry Classification System (NAICS) 2007, the North American Product Classification System (NAPCS) - Canada (Provisional Version 0.1), National Occupational Classification - Statistics (NOC-S) 2006 and Classification of Instructional Programs (CIP), Canada, 2000.

It contains explanations, definitions and examples of how the classification codes are mapped to the conceptual framework. It also contains a series of tables that contain codes, by classification system, which help illustrate the framework domains and sub-domains, and flags those codes that do not map well to the framework.

This series the Canadian Framework for Culture Statistics 2011 replaces the 2004 Canadian Framework for Culture Statistics (Catalogue 81-595-MIE2004021).

The first issue of this series presents the conceptual framework, including a definition of culture, domains and sub-domains, and criteria for their inclusion in culture. The second issue is a guide that maps the conceptual framework to selected standard classification systems. It is intended to foster a standard approach to the measurement of culture in Canada.

The Preview of Products and Services offers an overview of the proposed products and services that will be released based on the 2011 Census of Population and 2011 Census of Agriculture results. Information will include a description of the products and services, 'What's new?' in comparison to 2006, levels of geography, availability/delivery methods, release timeframe and pricing.

The preview is an Internet product, and a PDF version is available via the Internet. This product will be updated periodically as details regarding products and services become finalized.

The Preview of Census Geography Products and Services offers an overview of the proposed products and services that will be released based on the upcoming Census of Population. The geography product line includes geography concepts, enhancements and improvements to existing products, and a greater presence on the website. Reference maps and other geographic products are available in a variety of media. This document previews and highlights changes within the census geography product line.Technical limitations may result in some modifications to products described within this document. Users are encouraged to read the reference guides that accompany geography products for final product details.

The Postal Code Conversion File (PCCF) Reference Guide is available for the following product: Postal Code Conversion File (PCCF) (Catalogue No. 92-153-XCB). The Reference Guide describes how to use the product.

Most Geography products come with free reference guides that describe the content and applications of the product, as well as data quality, record layouts and other information. Reference guides are included as PDF files with the products when they are distributed. The guides are also accessible online.

This paper provides a preview of the comprehensive (historical) revision of the Canadian System National Accounts to be released beginning in June 2012. The last revision of this scope took place in 1997. The paper highlights the changes resulting from the adoption of SNA2008 which is the revised international standard for national accounting, along with statistical revisions arising from new and improved source data and methodologies. Updates to the classification systems used in the Canadian System of National Accounts are also presented along with a list of changes planned for 2014.