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This series of analytical reports provides an overview of the Canadian labour market experiences of immigrants to Canada, based on data from the Labour Force Survey. These reports examine the labour force characteristics of immigrants, by reporting on employment and unemployment at the Canada level, for the provinces and large metropolitan areas. They also provide more detailed analysis by region of birth, as well as in-depth analysis of other specific aspects of the immigrant labour market.

This report uses immigrant data series from the Labour Force Survey to provide a description of immigrants' labour-market outcomes, from 2006 to 2017.

The 2008 financial crisis demonstrated how quickly economic shocks can spread between sectors and countries, making it apparent that the existing set of macroeconomic statistics contained gaps for identifying such systemic issues. Users therefore require new products from the System of National Accounts that demonstrate the financial linkages between sectors in the economy. The Special Data Dissemination Plus (SDDS+) and G20 Data Gaps initiatives were established by the International Monetary Fund (IMF) to address some of these data gaps, and presented specific recommendations to G20 countries. One of these recommendations asks statistical agencies to begin compiling more detailed data on the interconnectedness of the economy by incorporating a "From-Whom-To-Whom" (FWTW) framework. This FWTW framework includes statistics that make counterparty information explicit, showing how various sectors of the economy are connected by financial interdependencies. In other words, the data presented on this new basis allows users to answer the question "whom is funding whom and with what financial instruments". In this visualisation tool, we present six instruments on a FWTW basis in a fully customizable pivot table.

This article presents the history of statistical law in Canada.

Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.

Sample coordination seeks to create a probabilistic dependence between the selection of two or more samples drawn from the same population or from overlapping populations. Positive coordination increases the expected sample overlap, while negative coordination decreases it. There are numerous applications for sample coordination with varying objectives. A spatially balanced sample is a sample that is well-spread in some space. Forcing a spread within the selected samples is a general and very efficient variance reduction technique for the Horvitz-Thompson estimator. The local pivotal method and the spatially correlated Poisson sampling are two general schemes for achieving well-spread samples. We aim to introduce coordination for these sampling methods based on the concept of permanent random numbers. The goal is to coordinate such samples while preserving spatial balance. The proposed methods are motivated by examples from forestry, environmental studies, and official statistics.

These last years, balanced sampling techniques have experienced a recrudescence of interest. They constrain the Horvitz Thompson estimators of the totals of auxiliary variables to be equal, at least approximately, to the corresponding true totals, to avoid the occurrence of bad samples. Several procedures are available to carry out balanced sampling; there is the cube method, see Deville and Tillé (2004), and an alternative, the rejective algorithm introduced by Hájek (1964). After a brief review of these sampling methods, motivated by the planning of an angler survey, we investigate using Monte Carlo simulations, the survey designs produced by these two sampling algorithms.

Many studies conducted by various electric utilities around the world are based on the analysis of mean electricity consumption curves for various subpopulations, particularly geographic in nature. Those mean curves are estimated from samples of thousands of curves measured at very short intervals over long periods. Estimation for small subpopulations, also called small domains, is a very timely topic in sampling theory.

In this article, we will examine this problem based on functional data and we will try to estimate the mean curves for small domains. For this, we propose four methods: functional linear regression; modelling the scores of a principal component analysis by unit-level linear mixed models; and two non-parametric estimators, with one based on regression trees and the other on random forests, adapted to the curves. All these methods have been tested and compared using real electricity consumption data for households in France.

In Italy, the Labor Force Survey (LFS) is conducted quarterly by the National Statistical Institute (ISTAT) to produce estimates of the labor force status of the population at different geographical levels. In particular, ISTAT provides LFS estimates of employed and unemployed counts for local Labor Market Areas (LMAs). LMAs are 611 sub-regional clusters of municipalities and are unplanned domains for which direct estimates have overly large sampling errors. This implies the need of Small Area Estimation (SAE) methods. In this paper, we develop a new area level SAE method that uses a Latent Markov Model (LMM) as linking model. In LMMs, the characteristic of interest, and its evolution in time, is represented by a latent process that follows a Markov chain, usually of first order. Therefore, areas are allowed to change their latent state across time. The proposed model is applied to quarterly data from the LFS for the period 2004 to 2014 and fitted within a hierarchical Bayesian framework using a data augmentation Gibbs sampler. Estimates are compared with those obtained by the classical Fay-Herriot model, by a time-series area level SAE model, and on the basis of data coming from the 2011 Population Census.

When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.

This table presents major field of study - Classification of Instructional Programs (CIP) 2016, occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree, age and sex for the population aged 15 years and over in private households of Canada.

This table presents employment income statistics, work activity during the reference year, occupation - National Occupational Classification (NOC) 2016, major field of study - Classification of Instructional Programs (CIP) 2016, highest certificate, diploma or degree, age and sex for the population aged 15 years and over in private households of Canada.

This table presents employment income statistics, STEM (science, technology, engineering and mathematics and computer sciences) and BHASE (business, humanities, health, arts, social science, and education) (non-STEM) groupings, major field of study - Classification of Instructional Programs (CIP) 2016, highest certificate, diploma or degree, location of study, immigrant status, class of worker and sex and age for the population aged 15 years and over who worked full-time full-year in 2015 in private households of Canada, provinces and territories and census metropolitan areas.

This table presents employment income statistics, work activity during the reference year, occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree, age and sex for the population aged 15 years and over in private households of Canada.

This table presents occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree, admission category and applicant type, period of immigration, age and sex for the immigrant population who landed between 1980 and 2016 aged 15 years and over, in private households of Canada, provinces and territories.

This table presents first official language spoken, occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree and immigrant status and admission category for the population aged 15 years and over who worked since 2015, in private households of Canada, provinces and territories, census metropolitan areas and census agglomerations.

This table presents mother tongue, industry - North American Industry Classification System (NAICS) 2012, knowledge of official languages, highest certificate, diploma or degree and immigrant status and admission category for the population aged 15 years and over who worked since 2015, in private households of Canada, provinces and territories, census metropolitan areas and census agglomerations.

This table presents first official language spoken, mobility status 5 years ago, place of residence 5 years ago, occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree, employment income statistics and age for the population aged 15 years and over who worked since 2015, in private households of Canada, provinces and territories.

This table presents first official language spoken, mobility status 5 years ago, place of residence 5 years ago, industry - North American Industry Classification System (NAICS) 2012, highest certificate, diploma or degree, employment income statistics and age for the population aged 15 years and over who worked since 2015, in private households of Canada, provinces and territories.

This table presents first official language spoken, mobility status 5 years ago, place of residence 5 years ago, language used most often at work, occupation - National Occupational Classification (NOC) 2016, highest certificate, diploma or degree and age for the population aged 15 years and over who worked since 2015, in private households of Canada, provinces and territories.

This series of analytical reports provides an overview of the Canadian labour market experiences of immigrants to Canada, based on data from the Labour Force Survey. These reports examine the labour force characteristics of immigrants, by reporting on employment and unemployment at the Canada level, for the provinces and large metropolitan areas. They also provide more detailed analysis by region of birth, as well as in-depth analysis of other specific aspects of the immigrant labour market.

This report uses immigrant data series from the Labour Force Survey to provide a description of immigrants' labour-market outcomes, from 2006 to 2017.

The 2008 financial crisis demonstrated how quickly economic shocks can spread between sectors and countries, making it apparent that the existing set of macroeconomic statistics contained gaps for identifying such systemic issues. Users therefore require new products from the System of National Accounts that demonstrate the financial linkages between sectors in the economy. The Special Data Dissemination Plus (SDDS+) and G20 Data Gaps initiatives were established by the International Monetary Fund (IMF) to address some of these data gaps, and presented specific recommendations to G20 countries. One of these recommendations asks statistical agencies to begin compiling more detailed data on the interconnectedness of the economy by incorporating a "From-Whom-To-Whom" (FWTW) framework. This FWTW framework includes statistics that make counterparty information explicit, showing how various sectors of the economy are connected by financial interdependencies. In other words, the data presented on this new basis allows users to answer the question "whom is funding whom and with what financial instruments". In this visualisation tool, we present six instruments on a FWTW basis in a fully customizable pivot table.

This article presents the history of statistical law in Canada.

Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.

Sample coordination seeks to create a probabilistic dependence between the selection of two or more samples drawn from the same population or from overlapping populations. Positive coordination increases the expected sample overlap, while negative coordination decreases it. There are numerous applications for sample coordination with varying objectives. A spatially balanced sample is a sample that is well-spread in some space. Forcing a spread within the selected samples is a general and very efficient variance reduction technique for the Horvitz-Thompson estimator. The local pivotal method and the spatially correlated Poisson sampling are two general schemes for achieving well-spread samples. We aim to introduce coordination for these sampling methods based on the concept of permanent random numbers. The goal is to coordinate such samples while preserving spatial balance. The proposed methods are motivated by examples from forestry, environmental studies, and official statistics.

These last years, balanced sampling techniques have experienced a recrudescence of interest. They constrain the Horvitz Thompson estimators of the totals of auxiliary variables to be equal, at least approximately, to the corresponding true totals, to avoid the occurrence of bad samples. Several procedures are available to carry out balanced sampling; there is the cube method, see Deville and Tillé (2004), and an alternative, the rejective algorithm introduced by Hájek (1964). After a brief review of these sampling methods, motivated by the planning of an angler survey, we investigate using Monte Carlo simulations, the survey designs produced by these two sampling algorithms.

Many studies conducted by various electric utilities around the world are based on the analysis of mean electricity consumption curves for various subpopulations, particularly geographic in nature. Those mean curves are estimated from samples of thousands of curves measured at very short intervals over long periods. Estimation for small subpopulations, also called small domains, is a very timely topic in sampling theory.

In this article, we will examine this problem based on functional data and we will try to estimate the mean curves for small domains. For this, we propose four methods: functional linear regression; modelling the scores of a principal component analysis by unit-level linear mixed models; and two non-parametric estimators, with one based on regression trees and the other on random forests, adapted to the curves. All these methods have been tested and compared using real electricity consumption data for households in France.

In Italy, the Labor Force Survey (LFS) is conducted quarterly by the National Statistical Institute (ISTAT) to produce estimates of the labor force status of the population at different geographical levels. In particular, ISTAT provides LFS estimates of employed and unemployed counts for local Labor Market Areas (LMAs). LMAs are 611 sub-regional clusters of municipalities and are unplanned domains for which direct estimates have overly large sampling errors. This implies the need of Small Area Estimation (SAE) methods. In this paper, we develop a new area level SAE method that uses a Latent Markov Model (LMM) as linking model. In LMMs, the characteristic of interest, and its evolution in time, is represented by a latent process that follows a Markov chain, usually of first order. Therefore, areas are allowed to change their latent state across time. The proposed model is applied to quarterly data from the LFS for the period 2004 to 2014 and fitted within a hierarchical Bayesian framework using a data augmentation Gibbs sampler. Estimates are compared with those obtained by the classical Fay-Herriot model, by a time-series area level SAE model, and on the basis of data coming from the 2011 Population Census.

When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.

This guide presents information of interest to users of data from the Survey of Household Spending (SHS). It includes descriptions of the survey terms and variables definitions as well as of the survey methodology and data quality. The guide also includes a section describing various examples of estimates that can be drawn from the survey data.

The Survey of Household Spending (SHS) conducted by Statistics Canada collects detailed spending information as well as selected information on dwelling characteristics and household equipment. This technical note provides additional information on the quality of the SHS data and their fitness for use.

The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 30 years. The IMDB combines administrative files on immigrant admissions and non-permanent resident permits from Immigration, Refugees and Citizenship Canada (IRCC) with tax files from the Canadian Revenue Agency (CRA). Information is available for immigrant taxfilers admitted since 1980. Tax records for 1982 and subsequent years are available for immigrant taxfilers. This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.

This technical reference guide is intended for users of the Education and Labour Market Longitudinal Platform (ELMLP). The data for the products associated with this release are derived from integrating the Registered Apprenticeship Information System (RAIS) with other administrative data on earnings. Statistics Canada has derived a series of indicators on educational pathways and labour market outcomes for registered apprentices by select trades, cohorts of apprentices, by sex, for Canada, all provinces, the Atlantic region, and for grouped territories.

This technical reference guide is intended for users of the Education and Labour Market Longitudinal Platform (ELMLP). The data for the products associated with this issue are derived from integrating Postsecondary Student Information System (PSIS) administrative data with other administrative data on earnings. Statistics Canada has derived a series of annual indicators on the labour market outcomes of public postsecondary graduates including median employment income by educational qualification, field of study, age group and sex for Canada, the provinces and the territories combined.

This document describes Statistics Canada's vision for a modernized national statistical system and its strategy for making it a reality in partnership with its many stakeholders.

This document describes Statistics Canada's vision for a modernized national statistical system and its strategy for making it a reality in partnership with its many stakeholders.

The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.

Data on income of census families, individuals and seniors are derived from income tax returns. The data for the products associated with this release are derived from the T1 file that Statistics Canada receives from Canada Revenue Agency (CRA) thirteen months after the end of the taxation year.