| Geography | CVs for operating revenue |
|---|---|
| percent | |
| Canada | 2.93 |
| Newfoundland and Labrador | 1.89 |
| Prince Edward Island | 0.00 |
| Nova Scotia | 0.61 |
| New Brunswick | 0.00 |
| Quebec | 2.32 |
| Ontario | 6.92 |
| Manitoba | 0.99 |
| Saskatchewan | 1.06 |
| Alberta | 5.19 |
| British Columbia | 7.06 |
| Yukon | 0.00 |
| Northwest Territories | 0.00 |
| Nunavut | 0.00 |
In order to produce optimal inter-city indexes, product comparisons were initially made by pairing cities that are in close geographic proximity. The resulting price level comparisons were then extended to include comparisons between all of the cities, using a chaining procedure. The following initial pairings were used:
Reliable inter-city price comparisons require that the selected products be very similar across cities. This ensures that the variation in index levels between cities is due to pure price differences and not to differences in the attributes of the products, such as size and/or quality.
Within each city pair, product price quotes were matched on the basis of detailed descriptions. Whenever possible, products were matched by brand, quantity and with some regard for the comparability of retail outlets from which they were selected.
Additionally, the target prices for this study are final prices and as such, include all sales taxes and levies applied to consumer products within a city. This can be an important source of variation when explaining differences in inter-city price levels.
It should be noted that price data for the inter-city indexes are drawn from the sample of monthly price data collected for the Consumer Price Index (CPI). Given that the CPI sample is optimized to produce accurate price comparisons through time, and not across regions, the number of matched price quotes between cities can be small. It should also be noted that, especially in periods when prices are highly volatile, the timing of the product price comparison can significantly affect city-to-city price relationships.
The weights used to aggregate the different product indexes within a city are based on the combined consumption expenditures of households living in the 12 cities tracked. As such, one set of weights is used for all 12 cities. Currently, 2015 expenditures are used to derive the weights. These expenditures are expressed in October 2016 prices.
The inter-city index for a particular city is compared to the weighted average of all 12 cities, which is equal to 100. For example, an index value of 102 for a particular city means that prices for the measured commodities are 2% higher than the weighted, combined city average.
Shelter prices were absent from the inter-city index program prior to 1999 because of methodological and conceptual issues associated with their measurement. The diverse nature of shelter means that accurate matches between cities are often difficult to make.
To account for some of these difficulties, a rental equivalence approach is used to construct the inter-city price indexes for owned accommodation. Such an approach uses market rents as an approximation to the cost of the shelter services consumed by homeowners. It is important to note that this approach may not be suitable for the needs of all users. For instance, since the rental equivalence approach does not represent an out-of-pocket expenditure, the indexes should not be used for measuring differences in the purchasing power of homeowners across cities.
| Average(kg/m2) | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 16.11 | 0.8 |
| ages 6 to 11 | ||
| Males | 17.76 | 1.6 |
| Females | 17.54 | 1.4 |
| ages 12 to 19 | ||
| Males | 22.25 | 1.4 |
| Females | 23.07 | 2.4 |
| ages 20 to 39 | ||
| Males | 27.37 | 1.5 |
| Females | 26.57 | 2.9 |
| ages 40 to 59 | ||
| Males | 28.40 | 1.3 |
| Females | 28.07 | 1.7 |
| ages 60 to 79 | ||
| Males | 28.39 | 0.9 |
| Females | 28.12 | 1.5 |
| Average (mmol/L) | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 1.30 | 1.6 |
| ages 6 to 11 | ||
| Males | 1.46 | 2.1 |
| Females | 1.40 | 1.7 |
| ages 12 to 19 | ||
| Males | 1.25 | 1.7 |
| Females | 1.37 | 2.2 |
| ages 20 to 39 | ||
| Males | 1.17 | 1.7 |
| Females | 1.48 | 3.2 |
| ages 40 to 59 | ||
| Males | 1.17 | 2.0 |
| Females | 1.49 | 1.6 |
| ages 60 to 79 | ||
| Males | 1.23 | 1.7 |
| Females | 1.54 | 2.2 |
For the fasted subsample selected among the 6 to 79 year old full sample participants, here are some estimation and accuracy measures for a selected variable (glucose).
| Average (mmol/L) | c.v.(%) | |
|---|---|---|
| ages 6 to 11 | ||
| Males | 4.8 | 0.8 |
| Females | 4.7 | 0.7 |
| ages 12 to 19 | ||
| Males | 4.9 | 0.8 |
| Females | 4.7 | 0.6 |
| ages 20 to 39 | ||
| Males | 5.1 | 2.0 |
| Females | 4.8 | 0.7 |
| ages 40 to 59 | ||
| Males | 5.4 | 1.8 |
| Females | 5.0 | 1.2 |
| ages 60 to 79 | ||
| Males | 6.1 | 3.2 |
| Females | 5.6 | 0.9 |
For the fatty acids subsample selected among the 20 to 79 year old full sample participants, here are some accuracy measures for a selected variable (omega-3 index measured in % weight of total fatty acids).
| Average (of %) | c.v.(%) | |
|---|---|---|
| ages 20 to 39 | ||
| Males | 4.17 | 2.8 |
| Females | 4.27 | 4.4 |
| ages 40 to 59 | ||
| Males | 4.40 | 2.1 |
| Females | 4.54 | 3.3 |
| ages 60 to 79 | ||
| Males | 5.09 | 3.1 |
| Females | 5.19 | 2.3 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 4.5 | 8 |
| ages 6 to 11 | ||
| Males | 3.1 | 8.8 |
| Females | 2.9 | 8.6 |
| ages 12 to 19 | ||
| Males | 1.9 | 8.8 |
| Females | 1.6 | 6.9 |
| ages 20 to 39 | ||
| Males | 1.8 | 14.2 |
| Females | 2.5 | 8.8 |
| ages 40 to 59 | ||
| Males | 2.1 | 8 |
| Females | 2.9 | 11.7 |
| ages 60 to 79 | ||
| Males | 2.2 | 11 |
| Females | 3.8 | 9.8 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 12 to 19 | ||
| Males | 0.1000 | 8.6 |
| Females | 0.0900 | 10.3 |
| ages 20 to 39 | ||
| Males | 0.1300 | 14.6 |
| Females | 0.1000 | 9 |
| ages 40 to 59 | ||
| Males | 0.1300 | 11 |
| Females | 0.1100 | 11.2 |
| ages 60 to 79 | ||
| Males | 0.1400 | 8.6 |
| Females | 0.1100 | 8.2 |
| Average | c.v.(%) | |
|---|---|---|
| 3 to 5 years | ||
| Both sexes | 455 | 1.5 |
| 6 to 11 years | ||
| Males | 467 | 1.6 |
| Females | 458 | 1.6 |
| 12 to 17 years | ||
| Males | 556 | 1.4 |
| Females | 551 | 1.1 |
| 18 to 39 years | ||
| Males | 570 | 1.1 |
| Females | 565 | 1.2 |
| 40 to 59 years | ||
| Males | 575 | 0.9 |
| Females | 575 | 1.3 |
| 60 to 79 years | ||
| Males | 594 | 1.4 |
| Females | 609 | 0.9 |
| Ratio | c.v.(%) | |
|---|---|---|
| ages 6 to 11 | ||
| Males | 0.839 | 0.5 |
| Females | 0.852 | 0.4 |
| ages 12-19 | ||
| Males | 0.824 | 0.8 |
| Females | 0.856 | 0.6 |
| ages 20 to 39 | ||
| Males | 0.783 | 0.8 |
| Females | 0.818 | 0.7 |
| ages 40 to 59 | ||
| Males | 0.759 | 0.9 |
| Females | 0.771 | 0.6 |
| ages 60 to 79 | ||
| Males | 0.734 | 0.6 |
| Females | 0.739 | 0.4 |
For the environmental lab data subsample selected among the 3 to 79 year old full sample participants, here are some accuracy measures for selected variables.
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 0.03 | 4.1 |
| ages 6 to 11 | ||
| Males | 0.03 | 3.9 |
| Females | 0.03 | 3.4 |
| ages 12 to 19 | ||
| Males | 0.03 | 3.6 |
| Females | 0.02 | 4.8 |
| ages 20 to 39 | ||
| Males | 0.05 | 5.5 |
| Females | 0.03 | 4.0 |
| ages 40 to 59 | ||
| Males | 0.06 | 4.2 |
| Females | 0.05 | 5.9 |
| ages 60 to 79 | ||
| Males | 0.07 | 3.7 |
| Females | 0.07 | 4.2 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 60.3 | 3.5 |
| ages 6 to 11 | ||
| Males | 66.2 | 2.8 |
| Females | 58.8 | 3.6 |
| ages 12 to 19 | ||
| Males | 65.2 | 5.0 |
| Females | 60.7 | 7.6 |
| ages 20 to 39 | ||
| Males | 72.1 | 8.7 |
| Females | 67.2 | 6.7 |
| ages 40 to 59 | ||
| Males | 74.0 | 9.6 |
| Females | 67.5 | 12.3 |
| ages 60 to 79 | ||
| Males | 64.0 | 6.2 |
| Females | 61.6 | 5.7 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 20 to 39 | ||
| Males | 2.0 | 10.9 |
| Females | 1.9 | 17.1 |
| ages 40 to 59 | ||
| Males | 3.5 | 11.1 |
| Females | 2.6 | 17.2 |
| ages 60 to 79 | ||
| Males | 3.5 | 14.9 |
| Females | 4.2 | 12.5 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| ages 3 to 5 | ||
| Both sexes | 0.0092 | 4.2 |
| ages 6 to 11 | ||
| Males | 0.0068 | 6.4 |
| Females | 0.0063 | 5.3 |
| ages 12 to 19 | ||
| Males | 0.0036 | 7.7 |
| Females | 0.0046 | 9.2 |
| ages 20 to 39 | ||
| Males | 0.0043 | 7.4 |
| Females | 0.0051 | 12.4 |
| ages 40 to 59 | ||
| Males | 0.0045 | 11.8 |
| Females | 0.0052 | 7.9 |
| ages 60 to 79 | ||
| Males | 0.0047 | 11.3 |
| Females | 0.0064 | 10.6 |
| Geometric mean | c.v.(%) | |
|---|---|---|
| All households | 1.149 | 6.5 |
| Geography | CV for operating revenue |
|---|---|
| percent | |
| Canada | 0.62 |
On April 13, 2017, the Government of Canada tabled the Cannabis Act (Bill C-45) in Parliament to legalize the production, distribution and consumption of cannabis. If legislation is approved by Parliament, the new status might come into effect by mid-2018.
Statistics Canada's mandate is to provide Canadians with high-quality statistical information on the country's economy and society. As legalization of non-medical cannabis has social and economic implications, the agency is preparing to track the impact of this change in Canada.
The Cannabis Stats Hub has been created as an innovative way to collect and disseminate information on the purchase and use of cannabis, through a centralized portal. The statistics produced will provide important data, ensuring that Canadians are well-positioned to understand the socioeconomic impacts of legalization in the event that Bill C-45 is passed.
Statistics Canada conducted in-person usability consultations. Participants were asked to complete a series of tasks and to provide feedback on the proposed website.
This consultation is now closed.
Individuals who wish to obtain more information or to take part in a consultation may contact Statistics Canada by sending an email to statcan.consultations-consultations.statcan@statcan.gc.ca.
Please note that Statistics Canada selects participants for each consultation to ensure feedback is sought from a representative sample of the target population for the study. Not all applicants will be asked to participate in a given consultation.
Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held or collected by the agency is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the Privacy notice.
Most participants successfully completed a series of tasks on various pages of the Cannabis Stats Hub. Overall, they indicated that the hub was simple enough to use and that they were pleased to see that so much cannabis-related information will be released in a centralized portal.
The participants mentioned that the graphs, charts and other visualizations in the hub helped better understand the data presented and are ideal for reports.
Statistics Canada thanks participants for their participation in this consultation. Their insights guide the agency's web development and ensure that the final products meet users' expectations.
| Aboriginal identity | Educational attainment | 2006 | 2016 |
|---|---|---|---|
| First Nations People | Bachelor's degree or higherFootnote 2 | 7.2% | 9.6% |
| College diploma | 17.3% | 21.4% | |
| Apprenticeship certificateFootnote 3 | 3.6% | 4.7% | |
| High school diploma | 20.1% | 24.5% | |
| No certificate, diploma or degree | 38.2% | 30.0% | |
| Métis | Bachelor's degree or higherFootnote 2 | 8.9% | 13.2% |
| College diploma | 21.1% | 25.6% | |
| Apprenticeship certificateFootnote 3 | 5.4% | 6.9% | |
| High school diploma | 24.3% | 26.5% | |
| No certificate, diploma or degree | 26.1% | 18.0% | |
| Inuit | Bachelor's degree or higherFootnote 2 | 3.9% | 5.3% |
| College diploma | 16.9% | 18.7% | |
| Apprenticeship certificateFootnote 3 | 3.6% | 5.0% | |
| High school diploma | 13.0% | 18.5% | |
| No certificate, diploma or degree | 50.8% | 43.9% | |
Source: Statistics Canada, Census of Population, 2006 and 2016. |
|||
| Census metropolitan area (CMA) | Average one-way commuting duration (in minutes) |
|---|---|
| Largest CMAs | |
| Toronto, Ont. | 34.0 |
| Montréal, Que. | 30.0 |
| Vancouver, B.C. | 29.7 |
| Large CMAs | |
| Ottawa–Gatineau, Ont./Que. | 27.3 |
| Calgary, Alta. | 26.5 |
| Edmonton, Alta. | 25.9 |
| Winnipeg, Man. | 24.0 |
| Québec, Que. | 22.4 |
| Greater Golden Horseshoe CMAs | |
| Oshawa, Ont. | 33.5 |
| Barrie, Ont. | 30.7 |
| Hamilton, Ont. | 28.4 |
| Brantford, Ont. | 24.1 |
| Guelph, Ont. | 23.9 |
| Kitchener–Cambridge–Waterloo, Ont. | 22.8 |
| Peterborough, Ont. | 22.7 |
| St. Catharines–Niagara, Ont. | 21.5 |
| Mid-sized CMAs | |
| Halifax, N.S. | 24.0 |
| Victoria, B.C. | 22.2 |
| London, Ont. | 21.9 |
| Saskatoon, Sask. | 19.7 |
| Windsor, Ont. | 18.9 |
| Regina, Sask. | 17.9 |
| Small CMAs, commuting relationships with neighbours | |
| Abbotsford–Mission, B.C. | 26.1 |
| Belleville, Ont. | 19.7 |
| Sherbrooke, Que. | 19.0 |
| Trois-Rivières, Que. | 18.8 |
| Other small CMAs | |
| Greater Sudbury, Ont. | 20.8 |
| Kingston, Ont. | 20.1 |
| Saint John, N.B. | 20.1 |
| Kelowna, B.C. | 19.8 |
| St. John's, N.L. | 19.3 |
| Saguenay, Que. | 17.7 |
| Thunder Bay, Ont. | 17.5 |
| Moncton, N.B. | 17.0 |
| Lethbridge, Alta. | 16.8 |
|
Note: Cities and surrounding areas with 100,000 or more residents are known as census metropolitan areas or CMAs. CMAs are not equal in their size or their infrastructure. To establish some more comparability, six groupings are used in the charts and tables of this release.
Source: Statistics Canada, Census of Population, 2016. |
|
| Geography | Percent | Significantly different from Canada level |
|---|---|---|
| Canada | 32.4 | - |
| Newfoundland and Labrador | 28.5 | -1 |
|
St. John's
|
27.6 | -1 |
| Prince Edward Island | 37.3 | +1 |
| Nova Scotia | 48.5 | +1 |
|
Halifax
|
49.3 | +1 |
| New Brunswick | 37.6 | +1 |
|
Moncton
|
32.6 | 0 |
|
Saint John
|
39.6 | +1 |
| Quebec | 24.4 | -1 |
|
Saguenay
|
32.9 | 0 |
|
Québec
|
25 | -1 |
|
Sherbrooke
|
23.9 | -1 |
|
Trois-Rivières
|
23.2 | -1 |
|
Montréal
|
23.7 | -1 |
|
Ottawa-Gatineau, Quebec part
|
22.7 | -1 |
| Ontario | 34.3 | +1 |
|
Ottawa-Gatineau, Ontario part
|
39.7 | +1 |
|
Kingston
|
49.2 | +1 |
|
Peterborough
|
39.4 | +1 |
|
Oshawa
|
34.5 | 0 |
|
Toronto
|
32 | 0 |
|
Hamilton
|
31.4 | 0 |
|
St. Catharines-Niagara
|
36.4 | 0 |
|
Kitchener-Cambridge-Waterloo
|
29 | -1 |
|
Brantford
|
34.6 | 0 |
|
Guelph
|
33 | 0 |
|
London
|
37.1 | +1 |
|
Windsor
|
39.8 | +1 |
|
Barrie
|
29.1 | 0 |
|
Greater Sudbury
|
35.5 | 0 |
|
Thunder Bay
|
39.3 | +1 |
| Manitoba | 31.1 | 0 |
|
Winnipeg
|
32.5 | 0 |
| Saskatchewan | 36.7 | +1 |
|
Regina
|
33.1 | 0 |
|
Saskatoon
|
38.6 | +1 |
| Alberta | 35.7 | +1 |
|
Calgary
|
38.5 | +1 |
|
Edmonton
|
36.1 | +1 |
| British Columbia | 34.1 | +1 |
|
Kelowna
|
30.8 | 0 |
|
Abbotsford-Mission
|
31.3 | 0 |
|
Vancouver
|
34.5 | +1 |
|
Victoria
|
37.5 | +1 |
| Yukon | 31.9 | 0 |
| Northwest Territories | 32.8 | 0 |
| Nunavut | 28.6 | 0 |
|
Note: A value of +1 means the difference observed is significantly higher, -1 means the difference is significantly lower and 0 means the difference is not statistically significant. Source: Canadian Community Health Survey, 2015-2016. Related products: Canadian Community Health Survey: Combined data, 2015/2016, The Daily |
||
Statement outlining results, risks and significant changes in operations, personnel and program
Statistics Canada ("the agency") is a member of the Innovation, Science and Economic Development portfolio.
Statistics Canada's role is to ensure that Canadians have access to a trusted source of statistics on Canada that meets their highest priority needs.
The agency's mandate derives primarily from the Statistics Act. The Act requires that the agency collects, compiles, analyzes and publishes statistical information on the economic, social, and general conditions of the country and its people. It also requires that Statistics Canada conduct the census of population and the census of agriculture every fifth year, and protects the confidentiality of the information with which it is entrusted.
Statistics Canada also has a mandate to co-ordinate and lead the national statistical system. The agency is considered a leader, among statistical agencies around the world, in co‑ordinating statistical activities to reduce duplication and reporting burden.
More information on Statistics Canada's mandate, roles, responsibilities and programs can be found in the 2017–2018 Main Estimates and in the Statistics Canada 2017–2018 Departmental Plan.
The quarterly financial report:
Statistics Canada has the authority to collect and spend revenue from other federal government departments and agencies, as well as from external clients, for statistical services and products.
This quarterly report has been prepared by management using an expenditure basis of accounting. The accompanying Statement of Authorities includes the agency's spending authorities granted by Parliament and those used by the agency consistent with the Main Estimates for the 2017–2018 fiscal year. This quarterly report has been prepared using a special purpose financial reporting framework designed to meet financial information needs with respect to the use of spending authorities.
The authority of Parliament is required before moneys can be spent by the Government. Approvals are given in the form of annually approved limits through appropriation acts or through legislation in the form of statutory spending authority for specific purposes.
The agency uses the full accrual method of accounting to prepare and present its annual departmental financial statements that are part of the departmental results reporting process. However, the spending authorities voted by Parliament remain on an expenditure basis.
This section highlights the significant items that contributed to the net decrease in resources available for the year, as well as actual expenditures for the quarter ended September 30.
This bar graph shows Statistics Canada's budgetary authorities and expenditures, in thousands of dollars, as of September 30, 2016 and 2017:
Chart 1 outlines the gross budgetary authorities, which represent the resources available for use for the year as of September 30.
Total authorities available for 2017–2018 have decreased by $264.7 million, or 29.6%, from the previous year, from $895.4 million to $630.7 million (Chart 1).
This net decrease was mostly the result of the following:
In addition to the appropriations allocated to the agency through the Main Estimates, Statistics Canada also has vote net authority within Vote 1, which entitles the agency to spend revenues collected from other federal government departments, agencies, and external clients to provide statistical services. Vote netting authority is stable at $120 million in each of the fiscal years 2016–2017 and 2017–2018.
Year-to-date net expenditures recorded to the end of the second quarter decreased by $152.6 million, or 34.6% from the previous year, from $441.0 million to $288.4 million (See Table A: Variation in Departmental Expenditures by Standard Object).
Most of the decrease in spending is due to the 2016 Census of Population program. Statistics Canada spent approximately 56.5% of its authorities by the end of the second quarter, compared with 56.9% in the same quarter of 2016–2017.
| Departmental Expenditures Variation by Standard Object | Q2 year-to-date variation between fiscal year 2016–2017 and 2017–2018 | |
|---|---|---|
| $'000 | % | |
| (01) Personnel | 5,393 | 2.0 |
| (02) Transportation and communications | -48,919 | -87.8 |
| (03) Information | -6,248 | -73.7 |
| (04) Professional and special services | -106,427 | -93.0 |
| (05) Rentals | -613 | -7.0 |
| (06) Repair and maintenance | -274 | -71.7 |
| (07) Utilities, materials and supplies | -629 | -57.0 |
| (08) Acquisition of land, buildings and works | 45 | - |
| (09) Acquisition of machinery and equipment | 1,352 | 65.3 |
| (10) Transfer payments | -100 | -100.0 |
| (12) Other subsidies and payments | 1,052 | 6,577.3 |
| Total gross budgetary expenditures | -155,368 | -33.2 |
| Less revenues netted against expenditures | ||
| Revenues | -2,791 | -10.5 |
| Total net budgetary expenditures | -152,577 | -34.6 |
| Note: Explanations are provided for variances of more than $1 million. | ||
The 2016 Census of Population reached the peak of its cyclical expenditures in 2016–2017, spending in 2017–2018 drops sharply as activities wound down.
01) Personnel: The increase is mainly due to the retroactive salary payments of signed collective agreements. This increase is partly offset by the decrease in expenditures of public servants whom were hired in 2016–2017 to conduct census-related activities.
02) Transportation and communications: The decrease is mainly due to reduced costs in postage and travel in 2016–2017 for census related activities.
03) Information: The decrease is mainly due to the printing of census material and the implementation of the census media plan in 2016–2017.
04) Professional and special services: The decrease is mainly due to the end of census collection and processing activities, which employed approximately 35,000 temporary staff in 2016–2017.
09) Acquisition of machinery and equipment: The increase is due to the purchase of new software licenses.
12) Other subsidies and payments: The increase is mainly due to the set-up of the salary overpayments resulting from Phoenix issues.
Revenues: The decrease is primarily the result of timing differences in the receipt of funds for scheduled key deliverables and of a change in presentation method.
Statistics Canada uses a risk-based decision-making process to conduct its business. In order to do so effectively, the agency identifies key corporate risks and develops corresponding mitigation strategies within its Corporate Risk Profile on an annual basis. At this point in time, none of the agency's key corporate risks involve significant financial risks.
In 2017–2018, Statistics Canada will continue the processing and analysis of Census program data, and dissemination of the remaining major 2016 census data releases. This year, six releases are planned. For the Census of Population, three releases took place so far (May, August and September) and the others are scheduled in October and November 2017. Regarding the Census of Agriculture, the release took place in May. This contrasts with last year, when Statistics Canada focused on data collection and processing activities of the 2016 Census program.
Since the start of the fiscal year, many of the agency's collective agreements were signed following negotiations between the unions and Treasury Board. As such, during the second quarter, Statistics Canada processed a significant number of retroactive payments to employees, increasing the agency's salary expenditures on a one-time basis. Further, employees' salaries were adjusted upwards as per the new agreements which will result in a higher overall salary for third quarter and going forward.
The original version was signed by
Anil Arora, Chief Statistician
Monia Lahaie, Acting Chief Financial Officer
Ottawa, Ontario
Date signed: November 22, 2017
| Total available for use for the year ending March 31, 2018Table footnote 1 | Used during the quarter ended September 30, 2017 | Year-to-date used at quarter-end | |
|---|---|---|---|
| in thousands of dollars | |||
| Vote 1 — Net operating expenditures | 445,202 | 149,839 | 255,663 |
| Statutory authority — Contribution to employee benefit plans | 65,492 | 16,373 | 32,746 |
| Total budgetary authorities | 510,694 | 166,212 | 288,409 |
|
|||
| Total available for use for the year ending March 31, 2017Table footnote 1 | Used during the quarter ended September 30, 2016 | Year-to-date used at quarter-end | |
|---|---|---|---|
| in thousands of dollars | |||
| Vote 1 — Net operating expenditures | 696,599 | 182,866 | 401,567 |
| Statutory authority — Contribution to employee benefit plans | 78,837 | 19,710 | 39,419 |
| Total budgetary authorities | 775,436 | 202,576 | 440,986 |
|
|||
| Planned expenditures for the year ending March 31, 2018 | Expended during the quarter ended September 30, 2017 | Year-to-date used at quarter-end | |
|---|---|---|---|
| in thousands of dollars | |||
| Expenditures: | |||
| (01) Personnel | 482,636 | 156,013 | 281,941 |
| (02) Transportation and communications | 22,158 | 3,341 | 6,782 |
| (03) Information | 4,756 | 1,344 | 2,224 |
| (04) Professional and special services | 43,366 | 4,716 | 7,957 |
| (05) Rentals | 16,803 | 4,743 | 8,172 |
| (06) Repair and maintenance | 2,777 | 54 | 108 |
| (07) Utilities, materials and supplies | 3,832 | 216 | 474 |
| (08) Acquisition of land, buildings and works | - | 42 | 45 |
| (09) Acquisition of machinery and equipment | 11,160 | 1,364 | 3,424 |
| (10) Transfer payments | 100 | - | - |
| (12) Other subsidies and payments | 43,106 | 947 | 1,068 |
| Total gross budgetary expenditures | 630,694 | 172,780 | 312,195 |
| Less revenues netted against expenditures: | |||
| Revenues | 120,000 | 6,568 | 23,786 |
| Total revenues netted against expenditures | 120,000 | 6,568 | 23,786 |
| Total net budgetary expenditures | 510,694 | 166,212 | 288,409 |
| Planned expenditures for the year ending March 31, 2017 | Expended during the quarter ended September 30, 2016 | Year-to-date used at quarter-end | |
|---|---|---|---|
| in thousands of dollars | |||
| Expenditures: | |||
| (01) Personnel | 537,192 | 136,949 | 276,548 |
| (02) Transportation and communications | 91,882 | 32,542 | 55,701 |
| (03) Information | 13,896 | 4,755 | 8,472 |
| (04) Professional and special services | 209,409 | 35,966 | 114,384 |
| (05) Rentals | 21,315 | 4,462 | 8,785 |
| (06) Repair and maintenance | 5,730 | 239 | 382 |
| (07) Utilities, materials and supplies | 6,408 | 609 | 1,103 |
| (08) Acquisition of land, buildings and works | - | - | - |
| (09) Acquisition of machinery and equipment | 9,342 | 1,047 | 2,072 |
| (10) Transfer payments | 207 | - | 100 |
| (12) Other subsidies and payments | 55 | 11 | 16 |
| Total gross budgetary expenditures | 895,436 | 216,580 | 467,563 |
| Less revenues netted against expenditures: | |||
| Revenues | 120,000 | 14,004 | 26,577 |
| Total revenues netted against expenditures | 120,000 | 14,004 | 26,577 |
| Total net budgetary expenditures | 775,436 | 202,576 | 440,986 |
