Labour Force Survey, November 2022 [Canada]

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The \"Sysmiss\" label in the Statistics section indicates the number of non-responding records for each variable, and the \"Valid\" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's

加拿大劳动力调查（Labour Force Survey, LFS）所产出的就业与失业估算数据，是衡量加拿大经济表现最及时且至关重要的指标之一。该调查于数据采集完成仅10日后便发布结果，是首批公开的主要月度经济数据序列。加拿大劳动力调查诞生于第二次世界大战结束后，旨在满足战时经济向和平时期经济转型过程中，对劳动力市场可靠且及时数据的迫切需求。该调查的核心目标是将劳动年龄人口划分为就业、失业及不在劳动力队伍中三类互斥群体，并为每一类群体提供描述性与解释性统计数据。LFS数据可用于计算广为人知的失业率，以及就业率、劳动参与率等其他标准劳动力市场指标。此外，LFS还可按行业、职业、公共与私营部门、工作时长等维度提供就业估算数据，且可结合多种人口统计特征进行交叉分类。调查估算范围覆盖加拿大全国、各省、各地区以及大量省以下次级行政区域。针对雇员群体，还会统计薪资水平、工会参与情况、岗位稳定性及工作场所规模等信息。这些数据被加拿大各级政府用于就业相关项目的评估与规划。加拿大就业与社会发展部（Employment and Social Development Canada）会依托区域失业率，为特定就业保险区域内的参保人员核定保险资格、赔付等级与保障时长。劳动力市场分析师、经济学家、咨询顾问、规划人员、预测专家及学术界人士，无论供职于公共部门还是私营部门，均会使用该数据集开展工作。注：本数据集已剔除缺失值，任何形式的无应答（如有效跳答、未填报）或“不知道/拒绝回答”均不得被编码为缺失值。统计模块中的“Sysmiss”标签代表各变量的无应答记录数，而“Valid”值则代表各变量的有效应答记录数。每个变量的总记录数由Sysmiss与Valid值共同构成。LFS修订说明：LFS估算数据此前基于2001年人口普查的人口基准值，现已调整为适配2006年人口普查的人口基准值，并回溯修订至1996年。原仅覆盖加拿大三大都会区的普查都会区（Census Metropolitan Area, CMA）变量现已扩展至九大都会区。两项基于2016年全国职业分类（National Occupation Classification, NOC）的职业变量已重新引入：涵盖10大类的通用分类变量（NOC_10）与涵盖40大类的细分分类变量（NOC_40）。新增了一项关于移民身份的变量（IMMIG），可区分新移民与定居移民。另有14项与家庭及配偶/伴侣相关的变量（原文未完整收尾）。