Annual Capital and Repair Expenditures Survey: Actual, Preliminary Actual and Intentions (CAPEX)

Detailed information for 2016





Record number:


This survey collects data on capital and repair expenditures in Canada. The information is used by Federal and Provincial government departments and agencies, trade associations, universities and international organizations for policy development and as a measure of regional economic activity. Your information may also be used by Statistics Canada for other statistical and research purposes.

Data release - May 10, 2016 (Intentions); February 28, 2017 (Preliminary Actual)


This annual survey collects data on the intentions for capital expenditures and the expenditures for the previous two years; on occasion, where economic changes justify the need, data on the revised intentions are also collected for the current year. Information on capital spending provides a useful indication of market conditions both in the economy at large and in particular industries. Since such expenditures account for a large and relatively variable proportion of gross domestic expenditures, the size and content of the investment program provides significant information about demands that have been placed upon the productive capabilities of the economy during the period covered by the survey. In addition, information on the relative size of the capital expenditures program planned, both in total and for individual industries, gives an indication of the views management hold on the future market demands in relation to present productive capacity. Inputs are used by the Canadian System of National Accounts, particularly in the Gross Domestic Product (GDP) and Balance of Payments. The information is used by Federal and Provincial governments and agencies, trade associations, universities and international organizations for policy development and as a measure of regional activity.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP program has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content.

The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.

Reference period: Fiscal year


  • Construction
  • Machinery and equipment
  • Non-residential building construction
  • Non-residential engineering construction
  • Repair and maintenance

Data sources and methodology

Target population

The target population comprises all business and government entities operating in Canada according to the North American Industry Classification System 2012 (NAICS) during the reference year. Outlays for used Canadian assets are excluded since they constitute a transfer of assets within Canada and have no effect on the aggregates of our domestic inventory. Assets imported from outside Canada are included as they increase our domestic inventory.

Instrument design

In January 2011, questionnaire design specialists tested the questionnaires with a variety of respondents.


This is a sample survey with a cross-sectional design.

This is a stratified random sample survey of business establishments classified both geographically and to the North American Industry Classification System (NAICS) Canada 2012, with a cross-sectional design.

Data sources

Data collection for this reference period: 2015-05-11 to 2016-01-25

Responding to this survey is mandatory.

Data are collected directly from survey respondents, extracted from administrative files and derived from other Statistics Canada surveys and/or other sources.

Data are collected directly from survey respondents, extracted from administrative files and derived from other Statistics Canada surveys and/or other sources.

Data are collected primarily through electronic questionnaire, while providing respondents with the option of receiving a paper questionnaire, replying by telephone interview or using other electronic filing methods. Follow-up for non-response and for data validation is conducted by email, telephone or fax.

Administrative data were obtained from Canada Revenue Agency, Institut de la Statistique du Québec and Natural Resources Canada.

The data are obtained under Section 13 of the Statistics Act and are incorporated directly into the production of capital expenditure estimates.

View the Questionnaire(s) and reporting guide(s).

Error detection

After the questionnaires have been completed and returned, the process of quality assurance continues through data editing. Data are screened at the micro level for internal, survey over survey and year over year inconsistencies.

Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g. CC >= 0, CM >= 0), linear equality edits (e.g. CC + CM = TC), linear inequality edits (e.g. TC >= CC), and equivalency edits (e.g. sum of capital construction assets = CC). When errors are found, they can be corrected using the failed edit follow up (FEFU) process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting, and processing.

Add-check edits identify expenditure data that are incorrectly reported in dollars rather than thousands, percentage data failing to add to 100 percent and/or inconsistencies related to the reported totals. Large difference edits evaluate the consistency of reported expenditures by comparing the current data with reports from a previous survey within the same year and from a different year. Also, the Hidiroglou-Berthelot method is used to detect outliers.


When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation are used to complete a questionnaire, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include: deterministic imputation, replacement using historical data (using preliminary or intentions data for the same reference year or using the previous year's data). When no reported historical information is available, the method used is imputation by Ratio which uses the relationship between the income and data provided by respondents. Note that units who can't be imputed with the previous enumerated methods undergo imputation by the mean. Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related, variables.

Imputation generates a complete and coherent micro data file that covers all survey variables.


When some enterprises have reported data combining many units located in more than one province or territory, or in more than one industrial classification, data allocation is required. Factors based on information from sources such as tax files and Business Register profiles are used to allocate the data reported on the combined report among the various estimation units where this enterprise is in operation.

The sample used for estimation comes from a two phase sampling process. An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the multiplication of the inverse of the probability of selection from each phase. It is then adjusted to take into account units that might have been misclassified (large units found in a stratum of small units for example). In addition, the sampling weights derived are modified and adjusted using updated information from taxation data. Using a statistical technique called calibration, the final set of weights is adjusted in such a way that the sample represents as closely as possible the taxation data of the population of this industry.

The weight calculated for each sampling unit indicates how many other units it represents. The final weights are usually either one or greater than one. Sampling units which are "Take-all" have sampling weights of one and only represent themselves; units with larger than expected size are seen as misclassified and their weight is usually adjusted so that they only represent themselves.

The sampling unit being the enterprise, it can represent numerous locations which might contribute to different parts of the population (different sub-industries, province/territory, etc.). Each location is considered an estimation unit. The characteristics of the estimation units are used to derive the domains of estimation, including the industrial classification and the geography. Estimation for the survey portion is done by simple aggregation of the weighted values of all sampled locations that are found in the domain of estimation. Estimates are computed for several domains of estimation such as industrial groups and provinces/territories, based on the most recent classification information available for the location and the survey reference period. It should be noted that this classification information may differ from the original sampling classification because records may have changed in size, industry, or location. Changes in classification are reflected immediately in the estimates.

In the case of the ineligible for sampling portion (also called take-none portion) of the target population defined in Statistics Canada's Business Activity, Expenditure and Output Survey, an adjustment derived from the relationship between two closely related variables - Total Capital (TC) collected at the survey and Cost of Acquisition (CoA) available from tax data - producing a model estimate for this portion of the population. The overall estimate includes the estimates from both the surveyed portion and the take-none portion.

The estimates for the construction sector (NAICS 23) and part of the agriculture sector (NAICS 11) are generated entirely from administrative data sources on the Cost of Acquisition (CoA) for NAICS 23 and from the Farm Financial Survey for a portion of NAICS 11.

Quality evaluation

Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of: individual responses (especially for the largest companies), general economic conditions, coherence with results from related economic indicator, historical trends, and information from other external sources (e.g. associations, trade publications, newspaper articles). After the estimation process, trend analysis for the various industries can be done. Commencing with an evaluation of the year over year (or percentage) change in each industry, provinces/territories that have industries or sub-industries experiencing unusual activity are highlighted. In addition, this type of analysis also identifies industries which have the largest impact on Canadian aggregates.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada generalized confidentiality system (G-CONFID). G-CONFID is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed to maximize response rates.

Measures of sampling error are calculated for each estimate. Also, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.

Both the sampling error and the response rate are combined into one quality rating code. This code uses letters that ranges from A to F where A means the data is of excellent quality and F means it is unreliable. Estimates with a quality of F will not be published. These quality rating codes can be requested and should always be taken into consideration.


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