The Monthly Survey of Manufacturing (MSM) publishes statistical series for manufacturers – sales of goods manufactured, inventories, unfilled orders, new orders and capacity utilization rates.

The MSM is a sample survey of approximately 6,500 Canadian manufacturing establishments, which are categorized into over 156 industries. Industries are classified according to the 2017 North American Industrial Classification System (NAICS). Seasonally adjusted series are available for the main aggregates.

An establishment comprises the smallest manufacturing unit capable of reporting the variables of interest. Data collected by the MSM provides a current 'snapshot' of sales of goods manufactured values by the Canadian manufacturing sector, enabling analysis of the state of the Canadian economy, as well as the health of specific industries in the short- to medium-term. The information is used by both private and public sectors including Statistics Canada, federal and provincial governments, business and trade entities, international and domestic non-governmental organizations, consultants, the business press and private citizens. The data are used for analyzing market share, trends, corporate benchmarking, policy analysis, program development, tax policy and trade policy.

1. Sales of goods manufactured

Sales of goods manufactured (formerly shipments of goods manufactured) are defined as the value of goods manufactured by establishments that have been shipped to a customer. Sales of goods manufactured exclude any wholesaling activity, and any revenues from the rental of equipment or the sale of electricity. Note that in practice, some respondents report financial transactions rather than payments for work done. Sales of goods manufactured are available by various NAICS levels, for Canada and broken down by province.

For the aerospace product and parts, and shipbuilding industries, the value of production is used instead of sales of goods manufactured. This value is calculated by adjusting monthly sales of goods manufactured by the monthly change in inventories of goods / work in progress and finished goods manufactured. Inventories of raw materials and components are not included in the calculation since production tries to measure "work done" during the month. This is done in order to reduce distortions caused by the sales of high value items when they are completed.

2. Inventories

Measurement of component values of inventory is important for economic studies as well as for derivation of production values. Respondents are asked to report their book values (at cost) of raw materials and components, any goods / work in progress, and finished goods manufactured inventories separately. Inventory levels are calculated on a Canada‑wide basis, not by province.

3. Orders

a) Unfilled Orders

Unfilled orders represent a backlog or stock of orders that will generate future sales of goods manufactured assuming that they are not cancelled. As with inventories, unfilled orders and new orders levels are calculated on a Canada‑wide basis, not by province.

The MSM produces estimates for unfilled orders for all industries except for those industries where orders are customarily filled from stocks on hand and order books are not generally maintained. In the case of the aircraft companies, options to purchase are not treated as orders until they are entered into the accounting system.

b) New Orders

New orders represent current demand for manufactured products. Estimates of new orders are derived from sales of goods manufactured and unfilled orders data. All sales of goods manufactured within a month result from either an order received during the month or at some earlier time. New orders can be calculated as the sum of sales of goods manufactured adjusted for the monthly change in unfilled orders.

4. Capacity utilization rate

The capacity utilization rate is the ratio between the actual production and the production capacity on a given date. Production capacity is defined as the maximum level of production a plant could reasonably expect to attain under realistic labour and operating conditions, fully utilizing the machinery and equipment in place.

Respondents are also asked to provide reasons if the plant has been operating at less than full capacity and reasons for a change in production capacity from the previous month.

5. Non-Durable / Durable goods

a) Non-durable goods industries include:

Food (NAICS 311),
Beverage and Tobacco Products (312),
Textile Mills (313),
Textile Product Mills (314),
Clothing (315),
Leather and Allied Products (316),
Paper (322),
Printing and Related Support Activities (323),
Petroleum and Coal Products (324),
Chemicals (325) and
Plastic and Rubber Products (326).

b) Durable goods industries include:

Wood Products (NAICS 321),
Non-Metallic Mineral Products (327),
Primary Metals (331),
Fabricated Metal Products (332),
Machinery (333),
Computer and Electronic Products (334),
Electrical Equipment, Appliance and Components (335),
Transportation Equipment (336),
Furniture and Related Products (337) and
Miscellaneous Manufacturing (339).

Survey design and methodology

Concept Review

In 2007, the MSM terminology was updated to be Chart of Accounts (COA) compliant. With the August 2007 reference month release the MSM has harmonized its concepts to the ASML. The variable formerly called "Shipments" is now called "Sales of goods manufactured". As well, minor modifications were made to the inventory component names. The definitions have not been modified nor has the information collected from the survey.

Methodology

The latest sample design incorporates the 2017 North American Industrial Classification Standard (NAICS). Stratification is done by province and industry with equal quality requirements for each province and each 3-digit (4-digit for transportation) NAICS group. Large size units are selected with certainty and small units are selected with a probability based on the desired quality of the estimate within a cell (an industry within a province).

The estimation system generates estimates using the NAICS. The estimates will also continue to be reconciled to the ASML. National estimates are produced for all variables collected by MSM, however only provincial estimates for sales of goods manufactured are produced. A measure of quality (CV) will also be produced.

Components of the Survey Design

Target Population and Sampling Frame

Statistics Canada's business register provides the sampling frame for the MSM. The target population for the MSM consists of all statistical establishments on the Business Register that are classified to the manufacturing sector (by NAICS). The sampling frame for the MSM is determined from the target population after subtracting establishments that represent the bottom 10% of the total manufacturing sales of goods manufactured estimate for each cell. These establishments were excluded from the frame so that the sample size could be reduced without significantly affecting quality.

The Sample

The MSM sample is a probability sample comprised of approximately 6,500 establishments. A new sample was chosen in the fall of 2017, followed by a six-month parallel run (from reference month September 2017 to reference month February 2018). The new sample was used officially for the first time for dissemination with the reference month December 2017.

This marks the first process of refreshing the MSM sample since 2012. The objective of the process is to keep the sample frame as fresh and up-to date as possible. All establishments in the sample are refreshed to take into account changes in their value of sales of goods manufactured, the removal of dead units from the sample and some small units are rotated out of the sample, while others are rotated into the sample.

Prior to selection, the sampling frame was subdivided into industry-province cells. Depending upon the number of establishments within each cell, further subdivisions were made to group similar sized establishments' together (called stratums). An establishment's size was based on revenue variables from the Business Register.

Each industry by province cell has a 'take-all' stratum composed of establishments sampled each month with certainty. This 'take-all' stratum is composed of establishments that are the largest statistical enterprises, and have the largest impact on estimates within a particular industry by province cell. These large statistical establishments comprise about 50% of the national manufacturing sales of goods manufactured estimates.

Each industry by province cell can have at most two 'take-some' strata. Not all establishments within these stratums need to be sampled with certainty. A random sample is drawn from the remaining strata. The responses from these sampled establishments are weighted according to the inverse of their probability of selection. In cells with take-some portion, a minimum sample size of 3 was imposed.

The take-none portion of the sample is now estimated from administrative data and as a result, 100% of the sample universe is covered. Estimation of the take-none portion also improved efficiency as a larger take-none portion was delineated and the sample could be used more efficiently on the smaller sampled portion of the frame.

Data Collection

The complete sample of establishments is sent out for data collection. Collection of the data is performed by Statistics Canada's Regional Offices. Respondents are sent an electronic or paper questionnaire or are contacted by telephone to obtain their sales, inventories, unfilled orders, capacity utilization rates, as well as to confirm the opening or closing of business trading locations. Collection also includes non-response follow-up. Collection of the data begins approximately 7 working days after the end of the reference month and continues for the duration of that calendar month.

New entrants to the survey are introduced to the survey via introductory questions that confirm the respondent's business activity and contact information.

If data are unavailable at the time of collection, a respondent's best estimates are also accepted, and are subsequently revised once the actual data become available.

To minimize total non-response for all variables, partial responses are accepted.

Use of Administrative Data

Managing response burden is an ongoing challenge for Statistics Canada. In an attempt to alleviate response burden, especially for small businesses, the MSM derives sales data for low-revenue establishments from Goods and Service Tax (GST) files using a ratio estimator. The ratio estimator also increases the precision of the surveyed portion of the estimate. For more information on the ratio estimator, see the section on estimation.

Data quality

Statistical Edit and Imputation

Data are analyzed within each industry-province cell. Extreme values are listed for inspection by the magnitude of the deviation from average behavior. Respondents are contacted to verify extreme values. Records that fail statistical edits are considered outliers and are not used for imputation.

Values are imputed for the non-responses, for establishments that do not report or only partially complete the survey form. A number of imputation methods are used depending on the variable requiring treatment. Methods include using industry-province cell trends and historical responses. Following imputation, the MSM staff performs a final verification of the responses that have been imputed.

Revisions

In conjunction with preliminary estimates for the current month, estimates for the previous three months are revised to account for any late returns. Data are revised when late responses are received or if an incorrect response was recorded earlier.

Estimation

Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. More specifically, the MSM uses a ratio estimator.

Ratio estimation consists of replacing the initial sampling weights (defined as the inverse of the probability of selection in the sample) by new weights in a manner that satisfies the constraints of calibration. Calibration ensures that the total of an auxiliary variable estimated using the sample must equal the sum of the auxiliary variable over the entire population, and that the new sampling weights are as close as possible (using a specific distance measure) to the initial sampling weights.

For example, suppose that the known population total of the auxiliary variable is equal to 100 and based on a sample the estimated total is equal to 90, so that we are underestimating by approximately 10%. Since we know the population total of the auxiliary variable, it would be reasonable to increase the weights of the sampled units so that the estimate would be exactly equal to it. Now since the variable of interest is related to the auxiliary variable, it is not unreasonable to believe that the estimate of the sales based on the same sample and weights as the estimate of the auxiliary variable may also be an underestimation by approximately 10%. If this is in fact the case, then the adjusted weights could be used to produce an alternative estimator of the total sales. This alternate estimator is called the ratio estimator.

In essence, the ratio estimator tries to compensate for 'unlucky' samples and brings the estimate closer to the true total. The improvement in variance will depend on the strength of the relationship between the variable of interest and the auxiliary data.

The take-none portion is taken into account by the ratio estimator. This is done by simply including the take-none portion in the control totals for the sample portion. By doing this, the weights for the sampled portion will be increased in such a way that the estimates will be adjusted to take into account the take-none portion.

The calculated weighted sales values are summed by domain, to produce the total sales estimates by each industrial group/geographic area combination and the other totals by industrial group. A domain is defined as the most recent classification values available from the BR for the unit and the survey reference period. These domains may differ from the original sampling strata because units may have changed size, industry or location. Changes in classification are reflected immediately in the estimates and do not accumulate over time.

For the capacity utilization rate, the estimate for a given domain is calculated by first calculating the total production and monthly production capacity for the domain and then by dividing the total production by the total monthly production capacity.

The measure of precision used for the MSM to evaluate the quality of a population parameter estimate and to obtain valid inferences is the variance. The variance from the survey portion is derived directly from a stratified simple random sample without replacement.

Sample estimates may differ from the expected value of the estimates. However, since the estimate is based on a probability sample, the variability of the sample estimate with respect to its expected value can be measured. The variance of an estimate is a measure of the precision of the sample estimate and is defined as the average, over all possible samples, of the squared difference of the estimate from its expected value.

Benchmarking

Up to and including 2003, the MSM was benchmarked to the Annual Survey of Manufactures and Logging (ASML). Benchmarking was the regular review of the MSM estimates in the context of the annual data provided by the ASML. Benchmarking re-aligned the annualized level of the MSM based on the latest verified annual data provided by the ASML.

Significant research by Statistics Canada in 2006-2007 was completed on whether the benchmark process should be maintained. The conclusion was that benchmarking of the MSM estimates to the ASML should be discontinued. With the refreshing of the MSM sample in 2007, it was determined that benchmarking would no longer be required (retroactive to 2004) because the MSM now accurately represented 100% of the sample universe. Data confrontation will continue between MSM and ASML to resolve potential discrepancies.

As of the December 2017 reference month, a new sample was introduced. It is standard practice that every few years the sample is refreshed to ensure that the survey frame is up to date with births, deaths and other changes in the population. The refreshed sample is linked at the detailed level to prevent data breaks and to ensure the continuity of time series. It is designed to be more representative of the manufacturing industry at both the national and provincial levels.

Data confrontation and reconciliation

Each year, during the period when the Annual Survey of Manufactures and Logging section set their annual estimates, the MSM section works with the ASML section to confront and reconcile significant differences in values between the fiscal ASML and the annual MSM at the strata and industry level.

The purpose of this exercise of data reconciliation is to highlight and resolve significant differences between the two surveys and to assist in minimizing the differences in the micro-data between the MSM and the ASML.

Sampling and Non-sampling Errors

Estimates derived from a sample survey are subject to a number of different kinds of errors. These errors can be broken down into two major types: sampling and non-sampling.

1. Sampling Errors

Sampling errors are an inherent risk of sample surveys. They result from the difference between the value of a variable if it is randomly sampled and its value if a census is taken (or the average of all possible random values). These errors are present because observations are made only on a sample and not on the entire population.

The sampling error depends on factors such as the size of the sample, variability in the population, sampling design and method of estimation. For example, for a given sample size, the sampling error will depend on the stratification procedure employed, allocation of the sample, choice of the sampling units and method of selection. (Further, even for the same sampling design, we can make different calculations to arrive at the most efficient estimation procedure.) The most important feature of probability sampling is that the sampling error can be measured from the sample itself.

2. Non-sampling Errors

Non-sampling errors result from a systematic flaw in the structure of the data-collection procedure or design of any or all variables examined. They create a difference between the value of a variable obtained by sampling or census methods and the variable's true value. These errors are present whether a sample or a complete census of the population is taken. Non-sampling errors can be attributed to one or more of the following sources:

a) Coverage error: This error can result from incomplete listing and inadequate coverage of the population of interest.

b) Data response error: This error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems.

c) Non-response error: Some respondents may refuse to answer questions, some may be unable to respond, and others may be too late in responding. Data for the non-responding units can be imputed using the data from responding units or some earlier data on the non-responding units if available.

The extent of error due to imputation is usually unknown and is very much dependent on any characteristic differences between the respondent group and the non-respondent group in the survey. This error generally decreases with increases in the response rate and attempts are therefore made to obtain as high a response rate as possible.

d) Processing error: These errors may occur at various stages of processing such as coding, data entry, verification, editing, weighting, and tabulation, etc. Non-sampling errors are difficult to measure. More importantly, non-sampling errors require control at the level at which their presence does not impair the use and interpretation of the results.

Measures have been undertaken to minimize the non-sampling errors. For example, units have been defined precisely and the most up-to-date listings have been used. Questionnaires have been carefully designed to minimize different interpretations. As well, detailed acceptance testing has been carried out for the different stages of editing and processing and every possible effort has been made to reduce the non-response rate as well as the response burden.

Measures of Sampling and Non-sampling Errors

1. Sampling Error Measures

The sample used in this survey is one of a large number of all possible samples of the same size that could have been selected using the same sample design under the same general conditions. If it was possible that each one of these samples could be surveyed under essentially the same conditions, with an estimate calculated from each sample, it would be expected that the sample estimates would differ from each other.

The average estimate derived from all these possible sample estimates is termed the expected value. The expected value can also be expressed as the value that would be obtained if a census enumeration were taken under identical conditions of collection and processing. An estimate calculated from a sample survey is said to be precise if it is near the expected value.

Sample estimates may differ from this expected value of the estimates. However, since the estimate is based on a probability sample, the variability of the sample estimate with respect to its expected value can be measured. The variance of an estimate is a measure of the precision of the sample estimate and is defined as the average, over all possible samples, of the squared difference of the estimate from its expected value.

The standard error is a measure of precision in absolute terms. The coefficient of variation (CV), defined as the standard error divided by the sample estimate, is a measure of precision in relative terms. For comparison purposes, one may more readily compare the sampling error of one estimate to the sampling error of another estimate by using the coefficient of variation.

In this publication, the coefficient of variation is used to measure the sampling error of the estimates. However, since the coefficient of variation published for this survey is calculated from the responses of individual units, it also measures some non-sampling error.

The formula used to calculate the published coefficients of variation (CV) in Table 1 is:

CV(X) = S(X)/X

where X denotes the estimate and S(X) denotes the standard error of X.

In this publication, the coefficient of variation is expressed as a percentage.

Confidence intervals can be constructed around the estimate using the estimate and the coefficient of variation. Thus, for our sample, it is possible to state with a given level of confidence that the expected value will fall within the confidence interval constructed around the estimate. For example, if an estimate of $12,000,000 has a coefficient of variation of 10%, the standard error will be $1,200,000 or the estimate multiplied by the coefficient of variation. It can then be stated with 68% confidence that the expected value will fall within the interval whose length equals the standard deviation about the estimate, i.e., between $10,800,000 and $13,200,000. Alternatively, it can be stated with 95% confidence that the expected value will fall within the interval whose length equals two standard deviations about the estimate, i.e., between $9,600,000 and $14,400,000.

Text table 1 contains the national level CVs, expressed as a percentage, for all manufacturing for the MSM characteristics. For CVs at other aggregate levels, contact Dissemination and Respondent Relations at (613) 951-9497, toll free: 1-866-873-8789 or by e-mail at statcan.meddissemination-mwtd-mceddiffusion-dfcg.statcan@statcan.c.ca

Text table 1: National Level CVs by Characteristic

National Level CVs by Characteristic
Table summary
The information is grouped by Month (appearing as row headers), Sales of goods manufactured, Raw materials and components inventories, Goods / work in process inventories, Finished goods manufactured inventories and Unfilled Orders (appearing as column headers).

Month	Sales of goods manufactured	Raw materials and components inventories	Goods / work in process inventories	Finished goods manufactured inventories	Unfilled Orders
	%
May 2017	0.60	1.03	0.87	1.26	0.85
June 2017	0.66	1.04	0.95	1.26	0.83
July 2017	0.64	1.05	1.01	1.26	0.85
August 2017	0.63	1.07	0.99	1.19	0.82
September 2017	0.64	1.09	1.01	1.21	0.81
October 2017	0.62	1.08	1.00	1.15	0.79
November 2017	0.62	1.07	1.01	1.11	0.84
December 2017	0.73	1.16	1.70	1.38	1.22
January 2018	0.62	1.10	1.50	1.42	1.19
February 2018	0.61	1.10	1.83	1.48	1.16
March 2018	0.61	1.18	1.57	1.37	1.17
April 2018	0.72	1.19	1.49	1.42	1.19
May 2018	0.74	1.11	1.48	1.45	1.09

2. Non-sampling Error Measures

The exact population value is aimed at or desired by both a sample survey as well as a census. We say the estimate is accurate if it is near this value. Although this value is desired, we cannot assume that the exact value of every unit in the population or sample can be obtained and processed without error. Any difference between the expected value and the exact population value is termed the bias. Systematic biases in the data cannot be measured by the probability measures of sampling error as previously described. The accuracy of a survey estimate is determined by the joint effect of sampling and non-sampling errors.

Sources of non-sampling error in the MSM include non-response error, imputation error and the error due to editing. To assist users in evaluating these errors, weighted rates are given in Text table 2. The following is an example of what is meant by a weighted rate. A cell with a sample of 20 units in which five respond for a particular month would have a response rate of 25%. If these five reporting units represented $8 million out of a total estimate of $10 million, the weighted response rate would be 80%.

The definitions for the weighted rates noted in Text table 2 follow. The weighted response and edited rate is the proportion of a characteristic's total estimate that is based upon reported data and includes data that has been edited. The weighted imputation rate is the proportion of a characteristic's total estimate that is based upon imputed data. The weighted take-none fraction rate is the proportion of the characteristic's total estimate modeled from administrative data.

Text Table 2: National Weighted Rates by Source and Characteristic

National Weighted Rates by Source and Characteristic
Table summary
The information is grouped by Characteristics (appearing as row headers), Data source, Response or edited, and Imputed (appearing as column headers).

Characteristics	Data source
	Response or edited	Imputed
	%
Sales of goods manufactured	90.5	9.5
Raw materials and components	83.2	16.8
Goods / work in process	87.0	13.0
Finished goods manufactured	83.9	16.1
Unfilled Orders	92.2	7.8
Capacity utilization rates	72.8	27.2

Joint Interpretation of Measures of Error

The measure of non-response error as well as the coefficient of variation must be considered jointly to have an overview of the quality of the estimates. The lower the coefficient of variation and the higher the weighted response rate, the better will be the published estimate.

Seasonal Adjustment

Economic time series contain the elements essential to the description, explanation and forecasting of the behavior of an economic phenomenon. They are statistical records of the evolution of economic processes through time. In using time series to observe economic activity, economists and statisticians have identified four characteristic behavioral components: the long-term movement or trend, the cycle, the seasonal variations and the irregular fluctuations. These movements are caused by various economic, climatic or institutional factors. The seasonal variations occur periodically on a more or less regular basis over the course of a year. These variations occur as a result of seasonal changes in weather, statutory holidays and other events that occur at fairly regular intervals and thus have a significant impact on the rate of economic activity.

In the interest of accurately interpreting the fundamental evolution of an economic phenomenon and producing forecasts of superior quality, Statistics Canada uses the X12-ARIMA seasonal adjustment method to seasonally adjust its time series. This method minimizes the impact of seasonal variations on the series and essentially consists of adding one year of estimated raw data to the end of the original series before it is seasonally adjusted per se. The estimated data are derived from forecasts using ARIMA (Auto Regressive Integrated Moving Average) models of the Box-Jenkins type.

The X-12 program uses primarily a ratio-to-moving average method. It is used to smooth the modified series and obtain a preliminary estimate of the trend-cycle. It also calculates the ratios of the original series (fitted) to the estimates of the trend-cycle and estimates the seasonal factors from these ratios. The final seasonal factors are produced only after these operations have been repeated several times. The technique that is used essentially consists of first correcting the initial series for all sorts of undesirable effects, such as the trading-day and the Easter holiday effects, by a module called regARIMA. These effects are then estimated using regression models with ARIMA errors. The series can also be extrapolated for at least one year by using the model. Subsequently, the raw series, pre-adjusted and extrapolated if applicable, is seasonally adjusted by the X-12 method.

The procedures to determine the seasonal factors necessary to calculate the final seasonally adjusted data are executed every month. This approach ensures that the estimated seasonal factors are derived from an unadjusted series that includes all the available information about the series, i.e. the current month's unadjusted data as well as the previous month's revised unadjusted data.

While seasonal adjustment permits a better understanding of the underlying trend-cycle of a series, the seasonally adjusted series still contains an irregular component. Slight month-to-month variations in the seasonally adjusted series may be simple irregular movements. To get a better idea of the underlying trend, users should examine several months of the seasonally adjusted series.

The aggregated Canada level series are now seasonally adjusted directly, meaning that the seasonally adjusted totals are obtained via X12-ARIMA. Afterwards, these totals are used to reconcile the provincial total series which have been seasonally adjusted individually.

For other aggregated series, indirect seasonal adjustments are used. In other words, their seasonally adjusted totals are derived indirectly by the summation of the individually seasonally adjusted kinds of business.

Trend

A seasonally adjusted series may contain the effects of irregular influences and special circumstances and these can mask the trend. The short term trend shows the underlying direction in seasonally adjusted series by averaging across months, thus smoothing out the effects of irregular influences. The result is a more stable series. The trend for the last month may be subject to significant revision as values in future months are included in the averaging process.

Real manufacturing sales of goods manufactured, inventories, and orders

Changes in the values of the data reported by the Monthly Survey of Manufacturing (MSM) may be attributable to changes in their prices or to the quantities measured, or both. To study the activity of the manufacturing sector, it is often desirable to separate out the variations due to price changes from those of the quantities produced. This adjustment is known as deflation.

Deflation consists in dividing the values at current prices obtained from the survey by suitable price indexes in order to obtain estimates evaluated at the prices of a previous period, currently the year 2012. The resulting deflated values are said to be "at 2012 prices". Note that the expression "at current prices" refer to the time the activity took place, not to the present time, nor to the time of compilation.

The deflated MSM estimates reflect the prices that prevailed in 2012. This is called the base year. The year 2012 was chosen as base year since it corresponds to that of the price indexes used in the deflation of the MSM estimates. Using the prices of a base year to measure current activity provides a representative measurement of the current volume of activity with respect to that base year. Current movements in the volume are appropriately reflected in the constant price measures only if the current relative importance of the industries is not very different from that in the base year.

The deflation of the MSM estimates is performed at a very fine industry detail, equivalent to the 6-digit industry classes of the North American Industry Classification System (NAICS). For each industry at this level of detail, the price indexes used are composite indexes which describe the price movements for the various groups of goods produced by that industry.

With very few exceptions the price indexes are weighted averages of the Industrial Product Price Indexes (IPPI). The weights are derived from the annual Canadian Input-Output tables and change from year to year. Since the Input-Output tables only become available with a delay of about two and a half years, the weights used for the most current years are based on the last available Input-Output tables.

The same price index is used to deflate sales of goods manufactured, new orders and unfilled orders of an industry. The weights used in the compilation of this price index are derived from the output tables, evaluated at producer's prices. Producer prices reflect the prices of the goods at the gate of the manufacturing establishment and exclude such items as transportation charges, taxes on products, etc. The resulting price index for each industry thus reflects the output of the establishments in that industry.

The price indexes used for deflating the goods / work in progress and the finished goods inventories of an industry are moving averages of the price index used for sales of goods manufactured. For goods / work in process inventories, the number of terms in the moving average corresponds to the duration of the production process. The duration is calculated as the average over the previous 48 months of the ratio of end of month goods / work in progress inventories to the output of the industry, which is equal to sales of goods manufactured plus the changes in both goods / work in progress and finished goods manufactured inventories.

For finished goods manufactured inventories, the number of terms in the moving average reflects the length of time a finished product remains in stock. This number, known as the inventory turnover period, is calculated as the average over the previous 48 months of the ratio of end-of-month finished goods manufactured inventory to sales of goods manufactured.

To deflate raw materials and components inventories, price indexes for raw materials consumption are obtained as weighted averages of the IPPI. The weights used are derived from the input tables evaluated at purchaser's prices, i.e. these prices include such elements as wholesaling margins, transportation charges, and taxes on products, etc. The resulting price index thus reflects the cost structure in raw materials and components for each industry.

The raw materials and components inventories are then deflated using a moving average of the price index for raw materials consumption. The number of terms in the moving average corresponds to the rate of consumption of raw materials. This rate is calculated as the average over the previous four years of the ratio of end-of-year raw materials and components inventories to the intermediate inputs of the industry.

Changes in legislation and the resulting change in the offence classification creates discontinuity in the historical record of particular criminal offences. Legislative changes to assault, sexual assault, theft, arson, mischief, prostitution and youth crime must be considered when making comparisons over time. Some of the more significant changes are as follows:

Sexual Assault: Bill C-127 (1983)
Bill C-127 abolished the offences of rape, attempted rape and indecent assault and introduced a three-tiered structure for sexual assault offences. The Bill also eased the circumstances under which police could lay charges in incidents of sexual and non-sexual assault.

Young Offenders Act (1984)
With the proclamation of the YOA in April 1984, 12 years became the minimum age for which criminal charges could be laid. However, the maximum age continued to vary until April 1985, when the maximum age of 17 (up to the 18th birthday) was established in all provinces and territories. Youths, as defined in this publication, refer to those aged 12 to 17 (inclusive). This definition applies to the target group who fall under the delegation of the Young Offenders Act (YOA).

Traffic Offences: Bill C-18 (1985)
In December 1985, Bill C18 made major legislative changes with respect to certain traffic offences (all 700 series offences). It imposed more stringent sentences for dangerous driving and drinking and driving. It also facilitated the enforcement of impaired driving laws by authorizing police to take blood and/or breath samples under certain circumstances. As a result, data previous to 1985 for traffic offences are not comparable and have not been presented.

Property value limits: Bill C-18 (1985) and Bill C-42 (1995)
In 1985, Bill C-18 altered the property value limits from under and over $200 to under and over $1,000. This applies to offences such as theft, possession of stolen goods, mischief and fraud. As of February 1995, Bill C-42 revised the property value limits to under and over $5,000.

Alternative measures: Bill C-41 (1996)
Bill C-41 was proclaimed into law September 3, 1996. One of its highlights was the introduction of “alternative measures” for adults, which provided ways of dealing with disputes and minor offences outside the formal court proceedings.

Firearms: Bill C-68 (1997)
Bill C-68, proclaimed on January 1, 1997, requires that all firearm owners must obtain a Firearms License by January, 2001. This license replaces the Firearms Acquisition Certificate in use since 1977. Commencing October 1, 1998, each weapon must be registered within five years and a Registration Certificate will be issued. Bill C-68 also provides for tougher penalties for using a firearm while committing a crime.

Controlled Drugs and Substances Act: Bill C-8 (1997)
This new legislation came into force on May 14, 1997. The Controlled Drugs and Substances Act (CDSA) repealed and replaced the Narcotic Control Act (NCA) and parts of the Food and Drugs Act (FDA) in 1996. With this change in legislation, offences related to the possession, trafficking and importation of certain controlled or restricted drugs not identified in the earlier statutes are now (since 1997) included in other drugs category. Hence, comparisons with years prior to 1997 should be made with caution.

Dangerous Operation Evading Police: Bill C-202 (2000)
Law C-202 came into effect March 30th, 2000. This legislation modifies section 249 of the Criminal Code, thus creating new offences of dangerous operation of a motor vehicle when used for evading police.

Youth Criminal Justice Act: Bill C-7 (2003)
The extrajudicial measures encouraged by the Youth Criminal Justice Act, proclaimed on April 1, 2003, include taking no further action, informal police warnings, referrals to community programs, formal police cautions, Crown cautions, and extrajudicial sanctions programs. It is presumed that extrajudicial measures are adequate to hold accountable non-violent offenders who have not previously been found guilty in court.

Street Racing: Bill C-19 (2006)
Bill C-19, proclaimed on December 14, 2006, addresses the street-racing problem by making four amendments to the Criminal Code: “Street-racing” has been defined, five new street-racing offences have been added, for three of the new offences, it provides maximum prison terms longer than those currently provided for dangerous operation or criminal negligence in the operation of a motor vehicle, and it introduces mandatory driving prohibition orders for a minimum period of time, with the length of the prohibition increasing gradually for repeat offences.

Unauthorized Recording of a Movie: Bill C-59 (2007)
Bill C-59, proclaimed on June 22, 2007, addresses the illegal recording of movies in theatres by creating two offences in the criminal code: recording for personal use of a movie shown in a theatre – liable to imprisonment for not more than two years, and recording for commercial purposes of a movie shown in a theatre – liable to imprisonment for not more than five years.

Tackling Violent Crime: Bill C-2 (2008)
As a result of Bill C-2, which was proclaimed on February 28, 2008, the age of consent was raised from 14 to 16 for the following Criminal Code offences: sexual interference, invitation to sexual touching, sexual exploitation, bestiality and exposure to person under 14. For sexual assault levels 1 to 3, the age changes for complainant (formerly 14) to under the age of 16.

Impaired operation and failure to provide blood sample now includes the separation between alcohol and drugs (or combination of drugs). Fail/refuse to provide breath sample and failure to comply or refusal (drugs) will now have a maximum penalty of 25 years.

New firearm offences will separate offences of breaking and entering by robbery to steal a firearm and to steal a firearm, which carry a maximum penalty of 25 years.

Tackling Violent Crime: Bill C-2 (2009)
As a result of Bill C-2, which was proclaimed on February 28, 2008, the UCR has also created a new code for sexual exploitation of a person with a disability. As well, two new Firearm violations have been added: Robbery to steal a firearm, and Break and Enter to steal a firearm.

Act to amend the Criminal Code (organized crime and protection of justice system participants) Bill C-14 (2009)
Bill C-14 officially came into effect on October 2, 2009. As a result, two new violation codes have been created: Assaulting with a weapon or causing bodily harm to a peace officer, and aggravated assault to a peace officer.

In 2002, legislative changes were made to include the use of the Internet for the purpose of committing child pornography offences. As such, the percent change in this offence is calculated from 2003 to 2009.

Codifying Identity Theft: Bill S4 (2010)
Bill S-4 officially came into effect on January 8, 2010. As a result, two new violation codes were created: Identity Theft and Identity Fraud.

Trafficking in Person’s under the age of 18: Bill C-268 (2010)
Bill C-268 officially came into effect on June 29, 2010. As a result, a new section was added to the Criminal Code; Section 279.011(1). This section will be coded into the existing UCR code of Trafficking in Persons.

An Act to amend the Criminal Code (suicide bombings): Bill S-215 (2010)
Bill S-215 officially came into effect on December 15, 2010. This enactment amends the Criminal Code to clarify that suicide bombings fall within the definition “terrorist activity”. As such they should be included in UCR codes: Participate in Terrorist Activity, Facilitate Terrorist Activity, and Instruct/Carry Out Terrorist Activity.

Tackling Auto Theft and Trafficking in Property Obtained by Crime: Bill S-9 (2011)
Bill S-9 officially came into effect on April 29, 2011. As a result, a new UCR violation code for Motor Vehicle Theft was created, replacing the current UCR violations of Motor Vehicle Theft over $5,000 and Motor Vehicle Theft $5,000 and under.

Possession of Stolen Goods is now separated into two categories; Possession of Stolen Goods over $5,000 and Possession of Stolen Goods $5,000 and under.

Three new UCR violation codes were also created: Altering/Destroying/Removing a vehicle identification number (VIN), Trafficking in Stolen Goods over $5,000, Trafficking in Stolen Goods $5,000 and under.

Amendment to the Controlled Drugs and Substances Act: Bill C-475 (2011)
Bill C-475 officially came into effect on June 26, 2011. As a result, a new section was added to the Criminal Code; Section 7.1(1). This section will be coded into the new UCR violation code of Precursor/Equipment (crystal meth, ecstasy).

The Safe Streets Act: Bill C-10 (2012)
Bill C-10 officially came into effect on August 9, 2012. As a result, two new sections were added to the Criminal Code; Section 172.2(1) and Section 171.1(1). Section 172.2(1) will be mapped to the existing UCR code of Luring a child via computer. Section 171.1(1) will be mapped to the new UCR violation code of Making Sexually Explicit material available to Children.

Combating Terrorism Act: Bill S-7 (2013)
Bill S-7 officially came into effect on July 15th, 2013. This enactment amends the Criminal Code to create offences of leaving or attempting to Canada to commit certain terrorism offences, and brought changes in relation to offences of harbouring terrorists. Seven new UCR violation codes were introduced mid-2013 in response to this legislation.

Mischief to war memorials: Bill C-217 (2014)
Under Criminal Code sections 430(4.11(a)), 430(4.11(b)) and 430 (4.2), Bill C-217 created new criminal offenses of mischief relating to war memorials (2177) when it came into force on the 19th of June, 2014. At the same time, the UCR violation code of mischief in relation to culture property was introduced to the survey.

Recruitment to Criminal Organizations: Bill C-394 (2014)
This bill came into force on September 6th, 2014 and makes the recruitment of members by a criminal organization a criminal offense under section 467.111 of the Criminal Code. Incidents of this offence will be coded under violation code 3843.

Protection of Communities and Exploited Persons Act: Bill C-36 (2014)
Bill C-36 came into effect in December 2014. The new legislation targets “the exploitation that is inherent in prostitution and the risks of violence posed to those who engage in it” (Criminal Code Chapter 25, preamble). New violations classified as “Commodification of sexual activity” under “violations against the person” include: the purchasing of sexual services or communicating for that purpose, receiving a material benefit deriving from the purchase of sexual services, procuring of persons for the purpose of prostitution, and advertising sexual services offered for sale. In addition, a number of other offences related to prostitution continue to be considered non-violent offences and are classified under “Other Criminal Code offences”. These include communicating to provide sexual services for consideration, and; stopping or impeding traffic for the purpose of offering, providing or obtaining sexual services for consideration. At the same time, the survey was amended to classify the violations codes of Parent or guardian procuring sexual activity, and Householder permitting prohibited sexual activity under “violations against the person”. The following violations officially expired on December 05, 2014: bawdy house, living off the avails of prostitution of a person under 18, procuring, obtains/communicates with a person under 18 for purpose of sex, and other prostitution. Police services are able to utilize these codes as their Records Management Systems are updated to allow it. As a result, these data should be interpreted with caution.

Effective December 2014, Bill C-36 amended the definition of the term “common bawdy house” in the Criminal Code to remove reference to prostitution. As a result of this amendment, the UCR violation of “Bawdy house” was terminated, and the new violation of “Common bawdy house” was introduced. Police services are able to utilize this amendment as their Records Management Systems are updated to allow it. As a result, these data should be interpreted with caution.

Protecting Canadians from Online Crime Act: Bill C-13 (2015)
On March 9, 2015, Bill C-13 Protecting Canadians from Online Crime Act came into effect. As a result, the law created a new criminal offence of non-consensual distribution of intimate images. It also clarified that Criminal Code offences of harassing / indecent communications can be committed by any means of telecommunication. Police services are able to utilize these amendments as their Records Management Systems are updated to allow them.

Tackling Contraband Tobacco Act: Bill C-10 (2015)
On April 10 2015, Bill C-10 Tackling Contraband Tobacco Act came into effect. As a result, this law created the Criminal Code offence of trafficking in contraband tobacco which is counted under the violation “Offences against the administration of law and justice”. Prior to April 2015, the offence was counted under “Excise Act”. As such, comparisons of these two violations to previous years should be made with caution.

Tougher Penalties for Child Predators Act: Bill C-26 (2015)
Coming into effect on July 17th, 2015, Bill C-26 increased the maximum penalties for certain sexual offences against children, including failure to comply with orders and probation conditions relating to sexual offences against children. In the UCR, the most serious violation is partially determined by the maximum penalty. As such, changes in maximum penalty may affect the most serious violation in an incident reported by police. Police services are able to utilize these amendments as their Records Management Systems are updated to allow them.

Bill C-51 – Anti-terrorism Act, 2015
Bill C-51 came into effect on July 18, 2015. As a result, a new violation code for the offence of “Advocating or promoting commission of terrorism offences” was added to the survey in reaction to this amendment to the Criminal Code.

An Act to amend the Criminal Code and to make related amendments to other Acts (medical assistance in dying) (2016)
On June 17, 2016, Bill C-14 “An Act to amend the Criminal Code to make related amendments to other Acts (medical assistance in dying)” came in effect. As a result, the law created new offences around for failing to comply with the safeguards which must be respected before medical assistance in dying may be provided to a person, for forging or destroying documents related to medical assistance in dying, for failing to provide the required information for the purpose of permitting the monitoring of medical assistance in dying and for contravening the regulations made by the Minister of Health respecting that information. Three new UCR2 violation codes were introduced in response to these amendments to the Criminal Code. Police services are able to utilize the survey revision as their Records Management Systems are updated to allow them.

Bill C-37 An Act to amend the Controlled Drugs and Substances Act and to make related Amendments to other Acts
On May 18, 2017, Bill C-37 "An Act to amend the Controlled Drugs and Substances Act and to make related amendments to other Acts" came into effect. As a result, the offence of possessing, producing, selling or importing anything knowing it will be used to produce or traffic in crystal meth or ecstasy was expanded to include all substances listed in Schedule I, II , III, IV or V of the Controlled Drugs and Substances Act.

Bill C-305 An Act to amend the Criminal Code (mischief)
On December 12, 2017, Bill C-305 “An Act to ament the Criminal Code (mischief)” came into effect. This enactment amended the offence of mischief to property primarily used for worship to include mischief in relation to property that is used by an identifiable group for educational purposes, administrative, social, cultural or sports activities or events or as residence for seniors.

Comparing UCR Data with Courts and Corrections Data

It is difficult to make comparisons between data reported by police and data from other sectors of the criminal justice system (i.e., courts and corrections). There is no single unit of count (i.e., incidents, offences, charges, cases or persons) which is defined consistently across the major sectors of the justice system. As well, charges actually laid can be different from the most serious offence by which incidents are categorized. In addition, the number and type of charges laid by police may change at the pre-court stage or during the court process. Time lags between the various stages of the justice process also make comparisons difficult.

Like so many other young people, you were probably excited to celebrate officially becoming an adult and finally being free to go where you want, when you want.

As a young adult, you may dream of becoming independent enough to leave the family nest. But do you have all the tools to fly on your own? As you study in order to join a competitive work environment, you must also earn money to pay for your studies and achieve your goals. How much does all this cost?

Living in an apartment

Many young people will have to leave home to study in another city. If this applies to you, here’s what to expect. In Canada, in 2016, tenants paid on average $1,002 per month for an apartment, including rent, electricity, heat, water and other services. In Montréal, these costs were $842 per month on average.^{Footnote 1} Add to this Internet access and telephone charges—musts for students—and costs for insurance, transportation, tuition and school materials, not to mention food. In this last category, if you’re like most Canadian households whose main earner is under 30 years of age, expect to pay an average of $7,484 a year.^{Footnote 2} When you add all that up, there really isn’t much money left for leisure.

Living with others

If you’re lucky enough to get money from registered education savings plans or from loans and bursaries, you already have a head start. Otherwise, you probably have a part-time job or a summer job to support yourself. Still, if you have to—or want to—leave home, the logical choice is often to live with roommates to lower costs. In addition to saving money, you’ll reduce your ecological footprint and do your part to save the planet!

Living with your parents or going back to live with them

Did you have to borrow money for your studies? You may have accumulated an average debt of $14,900 at the end of college, $26,300 at the end of a bachelor’s degree or $41,100 at the end of a doctorate.^{Footnote 3}

Since tuition and shelter costs rise faster than the inflation rate, living with your parents longer or returning to live with them for a while are becoming great options for reducing your expenses and saving money. This is what 63% of young adults 20 to 24 years of age did in 2016, up from 58% in 2001.^{Footnote 4}

Knowing how to adapt

Whatever your situation, moving toward greater autonomy involves knowing how to adapt to events and the unexpected, being flexible, inventive and, above all, resourceful. Combining all these strengths will put you on the road to success!

Status

This standard was approved as a departmental standard on February 21, 2011.

Definitions

Term	Definition
Economic territory	Economic territory is the area under the effective control of a single government or international organization. The economic territory of a country includes the land area, airspace, territorial waters and islands of that country as well as jurisdiction over fishing rights and rights to fuels or minerals whether on land or below the seabed. It also includes the country's territorial enclaves abroad. It excludes the territorial enclaves of foreign countries and international organizations in that country. The economic territory of a country includes free trade zones and offshore financial centres under the control of the government of that country even though different regulatory and tax regimes may apply.
Territorial enclaves	Territorial enclaves are clearly demarcated areas located outside a particular country that are owned or rented by the government of that country for diplomatic, military, scientific, or other purposes with the formal agreement of governments of the country where the areas are physically located. Territorial enclaves can also be owned or rented by international organizations. The territorial enclave of a country or international organization is under the effective control of that country or international organization and may be granted immunity from the laws of the host country. Territorial enclaves include embassies, consulates, military bases, scientific stations, information or immigration offices, aid agency offices, and central bank representative offices with diplomatic immunity.

Additional Information

The economic territory of an international organization consists of the territorial enclave or enclaves over which it has jurisdiction. These are excluded from the economic territory of Canada.

International merchandise trade statistics record goods that enter or leave the statistical territory, which is the territory with respect to which data is collected. For the international merchandise trade statistics of Canada, this statistical territory is analogous to the customs boundary. Within the System of National Accounts, trade statistics on goods is adjusted to approximate data for economic territory.

Conformity to relevant internationally recognized standards

The definition of economic territory conforms to the definition found in the System of National Accounts 2008^{Footnote 1}. The definition of the System of National Accounts is also referenced by the Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) ^{Footnote 2} and the International Merchandise Trade Statistics, Concepts and definitions 1998^{Footnote 3}.

Health Regions (HR) 2017 provides standard names and codes for Canada's health regions. Health regions are legislated administrative areas defined by provincial ministries of health. These administrative areas represent geographic areas of responsibility for hospital boards or regional health authorities. Health regions, being provincial administrative areas, are subject to change.

While the classification and its variant retain the same structures as in 2015, new reference maps have been created for HR 2017 to align health region boundaries with 2016 Census geography. Small boundary adjustments have been made in Manitoba and Saskatchewan to follow shorelines and conform to road networks.

Changes and corrections have been made to the following health region names:

• 1011 Eastern Regional Integrated Health Authority has been changed to Eastern Regional Health Authority (Health Regions 2017 and Health Regions for Alternate Reporting - Variant of HR 2017)
• 1012 Central Regional Integrated Health Authority has been changed to Central Regional Health Authority (Health Regions 2017 and Health Regions for Alternate Reporting - Variant of HR 2017)
• 1013 Western Regional Integrated Health Authority has been changed to Western Regional Health Authority (Health Regions 2017 and Health Regions for Alternate Reporting - Variant of HR 2017)
• 1014 Labrador-Grenfell Regional Integrated Health Authority has been changed to Labrador-Grenfell Regional Health Authority (Health Regions 2017 and Health Regions for Alternate Reporting - Variant of HR 2017)
• 3501 Érié St. Clair has been corrected to Érié St-Clair in French (Health Regions for Alternate Reporting - Variant of HR 2017)
• 3507 Toronto-Centre has been corrected to Centre-Toronto in French (Health Regions for Alternate Reporting - Variant of HR 2017)
• 3512 Simcoe-Nord Muskoka has been corrected to Simcoe Nord Muskoka in French (Health Regions for Alternate Reporting - Variant of HR 2017)
• 3551 Circonscription sanitaire de la cité d'Ottawa has been corrected to Circonscription sanitaire de la ville d'Ottawa in French (Health Regions 2017)
• 3555 Peterborough County-City Health Unit has been corrected to Peterborough County—City Health Unit in English (Health Regions 2017)
• 5930 Vancouver Central Health Authority has been corrected to Vancouver Coastal Health Authority (Health Regions 2017 and Health Regions for Alternate Reporting - Variant of HR 2017)

The first use of Health Regions (HR) 2017 and its variant was in the publication Health Regions: Boundaries and Correspondence with Census Geography (82-402-X).

The classification variant, Health Regions for Alternate Reporting - Variant of HR 2017, presents a second set of health regions for Ontario, the Local Health Integrated Networks (LHINs).