Analysis

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: This article examines trends in rates of employment and unemployment, as well as hourly wages and work hours, for the year 2023, and explores how disability intersects with age, sex, educational attainment, and racialized groups to influence labour market outcomes.

Description: With the proportion of small businesses making up nearly all of the employer businesses in Canada, small businesses are an important role in employing Canadians and are a significant driver towards economic recovery. This article provides insights on the expectations of small businesses as well as the unique conditions faced by these businesses in the second quarter of 2024. It involves an examination of the data produced by the Canadian Survey on Business Conditions.

Description: Labour Statistics at a Glance features short analytical articles on specific topics of interest related to Canada's labour market. The studies examine recent or historical trends using data produced by the Centre for Labour Market Information, i.e., the Labour Force Survey, the Survey of Employment Payrolls and Hours, the Job Vacancy and Wage Survey, the Employment Insurance Coverage Survey and the Employment Insurance Statistics Program.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: The purpose of this document is to define the concept of peer groups, to give an overview of how they are created and to demonstrate their usefulness. This paper presents the 2023 classification of the peer groups.

Description: The Health Research Working Paper Series publishes: analytical work-in-progress; background documentation for specific research projects (e.g methodological papers); lengthy reports intended for specific clients, and; compendiums of data tables. Publication in this series does not preclude publication of specific aspects of the work in a peer-reviewed journal.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.

Description: Non-probability samples are being increasingly explored in National Statistical Offices as an alternative to probability samples. However, it is well known that the use of a non-probability sample alone may produce estimates with significant bias due to the unknown nature of the underlying selection mechanism. Bias reduction can be achieved by integrating data from the non-probability sample with data from a probability sample provided that both samples contain auxiliary variables in common. We focus on inverse probability weighting methods, which involve modelling the probability of participation in the non-probability sample. First, we consider the logistic model along with pseudo maximum likelihood estimation. We propose a variable selection procedure based on a modified Akaike Information Criterion (AIC) that properly accounts for the data structure and the probability sampling design. We also propose a simple rank-based method of forming homogeneous post-strata. Then, we extend the Classification and Regression Trees (CART) algorithm to this data integration scenario, while again properly accounting for the probability sampling design. A bootstrap variance estimator is proposed that reflects two sources of variability: the probability sampling design and the participation model. Our methods are illustrated using Statistics Canada’s crowdsourcing and survey data.

Description: In this rejoinder, I address the comments from the discussants, Dr. Takumi Saegusa, Dr. Jae-Kwang Kim and Ms. Yonghyun Kwon. Dr. Saegusa’s comments about the differences between the conditional exchangeability (CE) assumption for causal inferences versus the CE assumption for finite population inferences using nonprobability samples, and the distinction between design-based versus model-based approaches for finite population inference using nonprobability samples, are elaborated and clarified in the context of my paper. Subsequently, I respond to Dr. Kim and Ms. Kwon’s comprehensive framework for categorizing existing approaches for estimating propensity scores (PS) into conditional and unconditional approaches. I expand their simulation studies to vary the sampling weights, allow for misspecified PS models, and include an additional estimator, i.e., scaled adjusted logistic propensity estimator (Wang, Valliant and Li (2021), denoted by sWBS). In my simulations, it is observed that the sWBS estimator consistently outperforms or is comparable to the other estimators under the misspecified PS model. The sWBS, as well as WBS or ABS described in my paper, do not assume that the overlapped units in both the nonprobability and probability reference samples are negligible, nor do they require the identification of overlap units as needed by the estimators proposed by Dr. Kim and Ms. Kwon.

Description: In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.

Description: Pseudo weight construction for data integration can be understood in the two-phase sampling framework. Using the two-phase sampling framework, we discuss two approaches to the estimation of propensity scores and develop a new way to construct the propensity score function for data integration using the conditional maximum likelihood method. Results from a limited simulation study are also presented.

Description: Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01-August 04, 2020.

Description: Our comments respond to discussion from Sen, Brick, and Elliott. We weigh the potential upside and downside of Sen’s suggestion of using machine learning to identify bogus respondents through interactions and improbable combinations of variables. We join Brick in reflecting on bogus respondents’ impact on the state of commercial nonprobability surveys. Finally, we consider Elliott’s discussion of solutions to the challenge raised in our study.

Description: This discussion summarizes the interesting new findings around measurement errors in opt-in surveys by Kennedy, Mercer and Lau (KML). While KML enlighten readers about “bogus responding” and possible patterns in them, this discussion suggests combining these new-found results with other avenues of research in nonprobability sampling, such as improvement of representativeness.

Description: Kennedy, Mercer, and Lau explore misreporting by respondents in non-probability samples and discover a new feature, namely that of deliberate misreporting of demographic characteristics. This finding suggests that the “arms race” between researchers and those determined to disrupt the practice of social science is not over and researchers need to account for such respondents if using high-quality probability surveys to help reduce error in non-probability samples.

Description: Nonprobability samples are quick and low-cost and have become popular for some types of survey research. Kennedy, Mercer and Lau examine data quality issues associated with opt-in nonprobability samples frequently used in the United States. They show that the estimates from these samples have serious problems that go beyond representativeness. A total survey error perspective is important for evaluating all types of surveys.

Description: The Aboriginal Peoples Survey (APS) is a national survey on the social and economic conditions of First Nations people living off reserve, Métis and Inuit. The objectives of the APS are to identify the needs of these Aboriginal groups and to inform policy and programs aimed at improving the well-being of Aboriginal peoples. The APS aims to provide current and relevant data for a variety of stakeholders, including Aboriginal organizations, communities, service providers, researchers, governments, and the general public.

The 2012 APS represents the fourth cycle of the survey and focuses on issues of education, employment and health of First Nations people living off reserve, Métis and Inuit aged 6 years and over.

The 2017 APS represents the fifth cycle of the survey and focuses on participation in the Canadian economy, transferable skills, practical training, use of information technology and Aboriginal language attainment of First Nations people living off reserve, Métis and Inuit aged 15 years and over.

Description:

The Geography working paper series is intended to stimulate discussion on a variety of topics covering conceptual, methodological or technical work to support the development and dissemination of the division's data, products and services. Readers of the series are encouraged to contact the Geography Division with comments and suggestions.

Description:

As Statistics Canada celebrates a significant milestone in 2018, it is time to take a look back at our history to see where we have been and what we have done over the past century. At the same time, it is a chance to reflect on where the agency is headed in the future. This series of articles shows how our work has evolved since 1918: where we started, how we have evolved and what we do now.

Description:

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.

Description:

Historical works allow readers to peer into the past, not only to satisfy our curiosity about “the way things were,” but also to see how far we’ve come, and to learn from the past. For Statistics Canada, such works are also opportunities to commemorate the agency’s contributions to Canada and its people, and serve as a reminder that an institution such as this continues to evolve each and every day.

On the occasion of Statistics Canada’s 100th anniversary in 2018, Standing on the shoulders of giants: History of Statistics Canada: 1970 to 2008, builds on the work of two significant publications on the history of the agency, picking up the story in 1970 and carrying it through the next 36 years, until 2008. To that end, when enough time has passed to allow for sufficient objectivity, it will again be time to document the agency’s next chapter as it continues to tell Canada’s story in numbers.

Description: This publication presents an overview of recent economic developments in the provinces and territories. The overview covers several broad areas: 1) gross domestic product (GDP) by income and by expenditure, 2) GDP by industry, 3) labour productivity and other related variables.

The publication examines trends in the major aggregates that comprise GDP, both income- and expenditure-based, as well as prices and the financing of economic activity by institutional sector. GDP is also examined by industry. The productivity estimates are meant to assist in the analysis of the short-run relationship among the fluctuations of output, employment, compensation and hours worked. Some issues also contain more technical articles, explaining national accounts methodology or analysing a particular aspect of the economy.

This publication carries the detailed analyses, charts and statistical tables that, prior to its first issue, were released in The Daily (11-001-XIE) under the headings Provincial Economic Accounts and Provincial Gross Domestic Product by industry.

Description:

Understanding the role of women in Canadian society and how it has changed over time is dependent on having information that can begin to shed light on the diverse circumstances and experiences of women. Women in Canada provides an unparalleled compilation of data related to women's family status, education, employment, economic well-being, unpaid work, health, and more.

Women in Canada allows readers to better understand the experience of women compared to that of men. Recognizing that women are not a homogenous group and that experiences differ not only across gender but also within gender groups, Women in Canada includes chapters on immigrant women, women in a visible minority, Aboriginal women, senior women, and women with participation and activity limitations.

Description:

The publication provides data users, health professionals and individual Canadians with geometric means and selected percentiles of blood and urine concentrations of selected environmental chemicals for the Canadian population by sex and age group. The results presented in this publication were collected during cycle 4 of the Canadian Health Measures Survey from January 2014 to December 2015.

Description: In 2018, Statistics Canada will celebrate its 100th anniversary. As we count down to this important milestone, we would like to use our data to highlight some of the sweeping changes that have had a lasting impact on Canadian society and economy.

Description:

The Record Linkage Project Process Model (RLPPM) was developed by Statistics Canada to identify the processes and activities involved in record linkage. The RLPPM applies to linkage projects conducted at the individual and enterprise level using diverse data sources to create new data sources to meet analytical and operational needs.