Gross Domestic Product by Industry - National (Monthly) (GDP)

Detailed information for March 2017

Status:

Active

Frequency:

Monthly

Record number:

1301

The purpose of this statistical program is to provide information for current economic analysis. It provides a measure of the economic production which takes place within the geographical boundaries of Canada from an industry point of view.

Data release - May 31, 2017

Description

Gross Domestic Product (GDP) by industry at basic prices is a measure of the economic production which takes place within the geographical boundaries of Canada. The term "gross" in GDP means that capital consumption costs, that is the costs associated with the depreciation of capital assets (buildings, machinery and equipment), are included. The production estimates are published for 192 separate industries and 81 aggregates to provide a variety of levels of detail useful for analysis of industrial economic performance, using the North American Industrial Classification System.

The GDP by industry measures provide an alternate dimension that supplements the income and expenditure-based GDP estimates, and constitute an extension (on a monthly basis) of the Canadian System of Macroeconomic Accounts Supply and Use Tables (SUT).

Statistical activity

The Canadian System of Macroeconomic Accounts (CSMA) provides a conceptually integrated framework of statistics for studying the state and behaviour of the Canadian economy. The accounts are centered on the measurement of activities associated with production of goods and services, the sales of goods and services in final markets, the supporting financial transactions, and the resulting wealth positions.

The Supply and Use tables are calculated at the national and provincial and territorial level, but on an annual basis only. They are available about two and half years after the end of the reference year; this is because of the delay in obtaining the needed source data and by the complex nature of producing such a detailed account. As a means of providing more up-to-date information to users for current analysis, two industry-based programs - one producing the country's current monthly GDP figures (record no. 1301), the other annual provincial-territorial estimates (record no. 1303) have been set up. These two programs, which can be viewed as extensions of the supply and use tables, use a set of indicators to project the GDP by industry benchmarks from the supply and use tables

Reference period: Month

Subjects

  • Economic accounts
  • Gross domestic product
  • Input-output accounts

Data sources and methodology

Target population

The target population is all statistical units resident in Canada involved in the economic activity of producing goods and services.

The observed population consists of all establishments in Canada. The establishment is the level at which the accounting data required to measure production is available. The establishment, as a statistical unit, is defined as the most homogeneous unit of production for which a household, business or government maintains accounting records from which it is possible to assemble all the data elements required to compile the full structure of the gross value of production (total sales or shipments, and inventories), the cost of materials and services, and labour and capital used in production.

Instrument design

This methodology does not apply.

Data sources

Data are collected from other Statistics Canada surveys and/or other sources.

The GDP measures rely heavily on a wealth of information from various areas of Statistics Canada, from other federal departments and agencies, from provincial government departments, and from private industry sources. This large amount of information is compiled, integrated and analysed as part of the complex process of arriving at GDP by industry.

For example, data from the Monthly Survey of Manufacturing are used for most (but not all) manufacturing industries. Data from the Survey of Employment, Payrolls and Hours (SEPH) are used for many service industries.

Error detection

Data at the working level industry, the lowest level of industry detail for which GDP estimates are compiled directly, are verified for large month-to-month percentage changes and potential issues arising from source data as well as analyzed for time series consistency, links to current economic events and with respect to coherence with related economic indicators not used in the derivation of the GDP estimates.

Imputation

This methodology type does not apply to this statistical program.

Estimation

Annual estimates of Gross Domestic Product (GDP) at basic prices (or value added) by industry can be measured directly from the Supply and Use Tables by summing the factor incomes and depreciation or indirectly by deducting the cost of the intermediate goods and services used in the production process from the value of gross production or output.

Constant price estimates of GDP by industry measure economic growth of industries with the effect of price variations removed. Annual constant price estimates of GDP are obtained by the double-deflation method, as described in publication Catalogue no. 15F0077G, A guide to deflating the input-output accounts: sources and methods.

Both the current and constant price estimates of GDP by industry are derived annually within the framework of the Supply and Use tables, and for all but the most recent two or three years. For the years following the most recent Supply and Use tables, and for the monthly estimates, real GDP by industry can be estimated by projecting the relationship between real gross output and real valued added, which holds over short periods of time. That is, the volume of value added generated from a given volume of output for a specific industry is generally constant over short periods of time, as major technological changes are required to change this relationship significantly.

To estimate the real value added of an industry on a monthly basis, indicators of real output, employment, or real inputs are used to project the relationship between these characteristics and value added, as determined from the deflated Supply and Use tables. See the document Catalogue no. 15-547-X, Gross Domestic Product by Industry: Sources and Methods for an overview of the general approach and of the various statistical techniques used in the derivation of monthly GDP. The document Catalogue no. 15-548-X, Gross Domestic Product by Industry: Sources and Methods with Industry Details provides a detailed description of the data sources used for each industry. A document containing an up-to-date summary of the data sources and methods used for compiling monthly national estimates of GDP by industry is included below in the documentation section.

Real measures of GDP by industry are calculated for individual industries and then aggregated to arrive at total economic growth. The monthly gross domestic product (GDP) by industry data are chained volume estimates with 2007 as their reference year. This means that the estimates for each industry and aggregate are obtained from a chained volume index multiplied by the industry's value added in 2007. For the period 2007 to 2013, the monthly estimates are benchmarked to annually chained Fisher volume indexes of GDP obtained from the constant-price supply and use tables. For the period starting with January 2014, the estimates are derived by chaining a Laspeyres volume index at 2013 prices to the prior period. This makes the monthly GDP by industry estimates more comparable with the expenditure-based GDP data, which are chained quarterly.

Quality evaluation

Data at the working level industry, the lowest level of industry detail for which GDP estimates are compiled directly, are verified for large month-to-month percentage changes and potential issues arising from source data as well as analyzed for time series consistency, links to current economic events and with respect to coherence with related economic indicators not used in the derivation of the GDP estimates.

Disclosure control

Gross domestic product by industry estimates are derived from supply and use tables that contain no suppressions for confidentiality. It has been determined that the distance between respondent information obtained from business surveys and administrative data and the aggregating structure and the conceptual and statistical measurement framework underlying the compilation of the supply and use framework is sufficiently large such that they mask the information provided by respondents and negate the need for data suppressions.

Revisions and seasonal adjustment

Revisions arise from updates to benchmark data, projectors, methodologies and seasonal adjustment. The revision policy is to revise back to previous year for the January to August reference months; for the September reference month, back to January of the fourth previous year; and for the October to December reference months, back to January of the current year. Comprehensive revisions are carried out occasionally.

Statistical revisions are carried out in order to incorporate the most recent information from surveys, taxation statistics, public accounts, censuses, etc., as well as new methodologies, data sources, concepts or definitions and the annual benchmarking process to the Supply and Use tables.

Statistics Canada uses the X-12-ARIMA seasonal adjustment method to seasonally adjust its time series. Monthly data are adjusted to reflect variation in the number of trading days within each month and seasonal adjustment factors are applied to derive the seasonally adjusted data. Any trading day adjustments generated by the X-12-ARIMA method are based not only on the number of days in the month but also on the relative importance of each day of the week.

Data accuracy

GDP by industry depends on the Supply and Use tables to which it is anchored. When necessary, the GDP by industry program may be required to use projectors of production which are of lower quality than the data sources underlying GDP from the Supply and Use tables; in these cases quality of the measure is lessened but still acceptable. As well, the availability of appropriate prices affects the reliability of the real GDP estimates. In general, the higher the level of aggregation, the more reliable are the estimates. There is a trade-off between timeliness and accuracy. As more robust data becomes available, estimates are revised and become more accurate until the benchmark Supply and Use Tables are published, approximately two and a half years after the monthly estimates of GDP by industry were first published.

In general, weaknesses in source data arise mainly from the following: a) undercoverage; b) inappropriate concepts and definitions. These are briefly discussed below:

a) Undercoverage - This weakness is normally corrected by inflating reported data by a factor that allows the data to represent the universe concerned.

b) Concepts and definitions not suitable for the CSMA - The data used in the derivation of monthly GDP are quite varied in coverage, details, definitions and concepts and often these factors do not coincide with those required. They must be thoroughly examined and adjusted for consistency and coverage using carefully designed estimating procedures.

No direct measures of the margin of error in the estimates can be calculated. Data reliability is a product of data integration and analysis inherent in the compilation of GDP by industry. They rely both on the quantitative attributes of the data sources used, such as sample size, response rate and coefficient of variation, and on the expert judgment of analysts who undertake data integration of these various sources.

In addition, as the monthly GDP by industry program relies on the annual estimates of real GDP obtained from the Supply and Use tables, the quality of these estimates has a direct influence on the quality of the monthly GDP measures. The quality ratings of GDP estimates from the current dollar Supply and Use tables for the latest benchmark year are available on request.

At the moment, there are no data reliability ratings for GDP by industry estimates in constant prices.

Documentation

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