New Lending Services Price Index

Methodology Summary Document

1. Context

Statistics Canada is in the process of developing a comprehensive suite of services producer price indexes. As part of this effort, the Finance, Insurance and Professional Services (FIPS) section is currently developing indexes for a variety of financial services, including banking, insurance, and securities brokerage and dealing.  More indexes will be developed as current projects move into production and priorities are identified.

In an effort to explore alternative administrative data sources, FIPS has obtained and analyzed data from the Bank of Canada’s A4 New Lending Report. The analysis has shown that it is possible to create a high quality New Lending Services Price Index (NLSPI) using this data.

2. Data

The data for the New Lending Services Price Index was obtained from the Bank of Canada’s Report on New Lending as well as from the Canadian System of Macroeconomic Accounts’ Gross Domestic Product by Income and Expenditure Accounts, and Financial Market Statistics used in the preparation of the Bank of Canada Review.

The Report on New Lending is collected monthly by the Bank of Canada through a survey of all Canadian chartered banks. Each bank is required to provide data on interest ratesNote1 and funds advanced for 10 lending products by 6 interest rate term maturities as well as for the aggregate of all products and all maturities (See Appendix A). In order to calculate prices for each lending product, a reference rate (See Section 5.3) was deducted from each product`s lending rate (by bank and maturity).

The reference rate was derived from data on Financial Market Statistics used in the preparation of the Bank of Canada Review and available on CANSIM. Certain market instruments were chosen and their yields were aggregated in order to produce the reference rate.

Since the value of money is eroded over time, a deflation factor is applied to the spread. The deflation factor is derived using the implicit price index for Final Domestic Expenditure from the Canadian System of Macroeconomic Accounts’ Gross Domestic Product by Income and Expenditure Accounts.

The data set is of a high quality and it presents significant detail in terms of maturity which allows for the construction of a mixed reference rate.

Furthermore, it imposes no further burden on respondents since they are already filling out this survey for the Bank of Canada.

3. Coverage

The NLSPI collects data on newly issued loans. Therefore, it is narrower in scope than a full Banking Services Price Index (BSPI) which includes all loans and deposits as well as explicit fees.

4. Usefulness

The primary purpose of the NLSPI is to provide supplemental information to help inform the deflation of output for the National Accounts. The industry in question is Canadian System of Macroeconomic Accounts (CSMA) industry BS5221A0 Banking and Other Depository Credit Intermediation. This corresponds to North American Industry Classification System (NAICS) industry 52211, Banking and industry 52219, Other Depository Credit Intermediation.  The NLSPI covers a portion of the output contained in MPS52X002 Residential mortgage services indirectly measured (FISIM) and MPS52X003 Other loan services indirectly measured (FISIM). Those commodities cover the output generated by all of the loans contained on the banks’ balance sheets while the NLSPI covers the output of only those loans issued in the reference period.  In terms of having a smaller coverage than the product heading, the NLSPI could be considered to meet the requirements of a B-method price. Note2

In terms of calculating a fuller BSPI, the NLSPI could serve as a component of a comprehensive index that would measure prices for all bank output including outstanding loans, deposits, and explicit fees.  Since the NLSPI measures the flow of lending at a detailed level of maturity, over time this data could be used to estimate weights for loan maturities when constructing a broader BSPI.

5. Methodology

5.1. New Lending Prices

The primary activity of a bank is to transform the deposits of savers into loans for borrowers. This is called depository credit intermediation. There are a variety of functions associated with this activity such as cheque clearing, debit card services, and credit analysis. For many of these services, banks charge explicit fees such as ATM fees and wire transfer fees. Banks also earn significant income indirectly by collecting more in interest on loans then they pay on deposits. This spread between interest income and interest expense is called Financial Intermediation Services Indirectly Measured (FISIM). The NLSPI seeks to measure changes over time in the portion of this spread associated with newly issued loans.

5.2. User-Cost of Money

Prices in banking are measured using the User-Cost of Money approach. The user-cost approach defines the price of a loan as the difference between the effective rateNote3 on that loan and some reference rate plus explicit fees per dollar of loan.

plj = (rlj - rj) + slj

where plj is price for each bank l’s product, j; rlj is the interest rate for each bank l’s product, j; rj represents the reference rate, and slj represent the explicit fees.

For the purposes of the NLSPI, explicit fees are not included and we are left with simple loan margins.

plj= (rlj - rj)

Since the value of money is eroded over time, a deflation factor is applied to the spread.

This gives:

plj = (rlj - rj) * d

All variables in this equation are described above, and d is the estimated monthly value rate of the implicit price index for Final Domestic Expenditure. For a more detailed explanation of deflation see Appendix B.

5.3. Reference Rate

The reference rate that we have been discussing is defined as the theoretical opportunity cost of money. There exists no consensus, either in the literature or international practice, as to the choice of reference rate. While we have experimented with several individual reference rates as well as with a multiple reference rate approach that matches reference rates to maturities, we have opted to use a single, mixed maturity reference rate.  This method is based on the Report of the Intersecretariat Working Group on National Accounts (ISWGNA) Task Force on FISIM and the method has been reviewed by Statistics Canada’s Price Measurement Advisory Committee.

The mixed rate is constructed by taking the yields of a variety of market instruments each of which matches one of the loan maturities (See Appendix C). The yields are aggregated using the trailing 12 month funds advanced of the matching maturities as quantity weights.Note4

This brings the price calculation to:

plj = (rlj - r) * d

where plj, rlj and d remain as described above, and where r represents the mixed reference rate.

5.4. Aggregation

The NLSPI is currently produced using a commodity-based aggregation which allows for the production of a variety of sub-indexes. Although it might be possible to provide series at a more detailed level to the CSMA, we are not currently planning to publish sub-indexes.

In order to weigh the prices from the microdata level, we use derived revenues as weights. Revenues are derived by multiplying the derived prices by the funds advanced for each product at each maturity for each bank.

Appendix A
A1 Products

Section I – Interest rates – Percentages

  • To individuals:
    • Personal loan plans
    • Personal lines of credit, secured
    • Personal lines of credit, unsecured
    • Other personal
    • Residential mortgages, insured
    • Residential mortgages, uninsured
  • Total personal loans and mortgages
  • To the business sector
    • Loans to regulated non-bank financial institutions
    • Lease receivables
    • Loans to individuals and others for business purposes
    • Non-residential mortgages
  • Total selected business loans

Section II – Funds advanced – Thousands of dollars

  • To individuals:
    • Personal loan plans
    • Personal lines of credit, secured
    • Personal lines of credit, unsecured
    • Other personal
    • Residential mortgages, insured
    • Residential mortgages, uninsured
  • Total personal loans and mortgages
  • To the business sector
    • Loans to regulated non-bank financial institutions
    • Lease receivables
    • Loans to individuals and others for business purposes
    • Non-residential mortgages
  • Total selected business loans

A2 Maturities

All
Variable rate
Fixed rate <1 year
Fixed rate 1 to <3 years
Fixed rate 3 to <5 years
Fixed rate 5 to <7 years
Fixed rate 7 years and over

Appendix B
Deflation

The APR that is reported on the A4 is the interest rate banks quote to their clients. It is equal to the effective rate if and only if there is a single interest payment made annually without compounding. The user-cost approach requires the use of effective interest rates but since those are not available in the A4, we are using the APR as a proxy. In that case, it’s important to keep in mind that the APR is a function of interest income and funds advanced:

APR = (Interest Income / Funds Advanced)

The same is also true of the assets that make up the reference rates:

Yield = (Interest income / Funds advanced)

As discussed in Section 5.2, the funds advanced must be discounted to account for the decline in the purchasing power of money over time on a cumulative basis.

For the purposes of the NLSPI, we have chosen the monthly estimatedNote5 level of the implicit price index for Final Domestic Expenditure as the deflator. We then apply this value to the spreadNote6 to obtain the final price for month t:

Pt = (APRt – rt) * dt

dt = dt-1* ht

where dt = 1 in the first month. In subsequent months, dt-1 represents the previous month’s deflator, and ht is the monthly growth rate of GDI calculated as the cubic root of the quarterly growth rate.

Appendix C
Reference rate for the NLSPI

Mixed Reference Rate

The reference rate for the NLSPI is a weighted average of the yields to maturity for the market instruments listed below. The weight of each instrument is the 12-month trailing funds advanced for the corresponding term maturity. The data is obtained from CANSIM Table 176-0043.

This table displays the results of reference rate for the nlspi. The information is grouped by maturity (appearing as row headers), market rate and cansim vector (appearing as column headers).
Maturity Market Rate CANSIM Vector
Variable Rate Overnight Rate V122514
Fixed Rate <1 year 6 month Treasury Bill V122532
Fixed Rate 1 to <3 years 1-3 year Government Bonds V122558
Fixed Rate 3 to <5 years 3-5 year Government Bonds V122485
Fixed Rate 5 to <7 years 5-10 year Government Bonds V122486
Fixed Rate 7 years and over 5-10 year Government Bonds V122486

Notes:

  1. The interest rates that are provided are in the form of Annual Percentage Rates (APRs) which is the same as the effective rate only if the loan is held for exactly one year with a single interest payment without default.
  2. See Handbook on Price and Volume Measures in National Accounts, p. 30, 31.
  3. Interest Income divided by Balance of Outstanding Loans.
  4. Note that for reference year 2011; the reference rate is weighted using the fixed value of 2011 funds advanced.
  5. Since this implicit price index is only available quarterly, we take the third root of the quarterly growth rate and then multiply it by the previous month’s deflator to arrive at a cumulative monthly deflator.
  6. Deflating the funds advanced is equivalent to multiplying the spread by the monthly deflator.

New Lending Services Price Index

Methodology Summary Document

1. Context

Statistics Canada is in the process of developing a comprehensive suite of services producer price indexes. As part of this effort, the Finance, Insurance and Professional Services (FIPS) section is currently developing indexes for a variety of financial services, including banking, insurance, and securities brokerage and dealing.  More indexes will be developed as current projects move into production and priorities are identified.

In an effort to explore alternative administrative data sources, FIPS has obtained and analysed data from the Bank of Canada’s A4 New Lending Report. The analysis has shown that it is possible to create a high quality New Lending Services Price Index (NLSPI) using this data.

2. Data

The data for the New Lending Services Price Index was obtained from the Bank of Canada’s Report on New Lending as well as from the Canadian System of National Accounts’ Gross Domestic Product by Income and Expenditure Accounts, and Financial Market Statistics used in the preparation of the Bank of Canada Review.

The Report on New Lending is collected monthly by the Bank of Canada through a survey of all Canadian chartered banks. Each bank is required to provide data on interest ratesNote1 and funds advanced for 10 lending products by 6 interest rate term maturities as well as for the aggregate of all products and all maturities (See Appendix A). In order to calculate prices for each lending product, a reference rate (See section 5.3.) was deducted from each product`s lending rate (by bank and maturity).

The reference rate was derived from data on Financial Market Statistics used in the preparation of the Bank of Canada Review and available on CANSIM. Certain market instruments were chosen and their yields were aggregated in order to produce the reference rate.

Since the value of money is eroded over time, a deflation factor is applied to the spread. The deflation factor is derived using the implicit price index for Final Domestic Expenditure from the Canadian System of National Accounts’ Gross Domestic Product by Income and Expenditure Accounts.

The data set is of a high quality and it presents significant detail in terms of maturity which allows for the construction of a mixed reference rate.

Furthermore, it imposes no further burden on respondents since they are already filling out this survey for the Bank of Canada.

3. Coverage

The NLSPI collects data on newly issued loans. Therefore, it is narrower in scope than a full Banking Services Price Index (BSPI) which includes all loans and deposits as well as explicit fees.

4. Usefulness

The primary purpose of the NLSPI is to provide supplemental information to help inform the deflation of output for the National Accounts. The industry in question is Canadian System of National Accounts (CSNA) industry BS5221A0 Banking and Other Depository Credit Intermediation. This corresponds to North American Industry Classification System (NAICS) industry 52211, Banking.  The NLSPI covers a portion of the output contained in MPS520002 Residential mortgage services indirectly measured (FISIM) and MPS520003 Other loan services indirectly measured (FISIM). Those commodities cover the output generated by all of the loans contained on the banks’ balance sheets while the NLSPI covers the output of only those loans issued in the reference period.  In terms of having a smaller coverage than the product heading, the NLSPI could be considered to meet the requirements of a B-method price.Note2

In terms of calculating a fuller BSPI, the NLSPI could serve as a component of a comprehensive index that would measure prices for all bank output including outstanding loans, deposits, and explicit fees.  Since the NLSPI measures the flow of lending at a detailed level of maturity, over time this data could be used to estimate weights for loan maturities when constructing a broader BSPI.

5. Methodology

5.1. New Lending Prices

The primary activity of a bank is to transform the deposits of savers into loans for borrowers. This is called depository credit intermediation. There are a variety of functions associated with this activity such as cheque clearing, debit card services, and credit analysis. For many of these services, banks charge explicit fees such as ATM fees and wire transfer fees. Banks also earn significant income indirectly by collecting more in interest on loans then they pay on deposits. This spread between interest income and interest expense is called Financial Intermediation Services Indirectly Measured (FISIM). The NLSPI seeks to measure changes over time in the portion of this spread associated with newly issued loans.

5.2. User-Cost of Money

Prices in banking are measured using the User-Cost of Money approach. The user-cost approach defines the price of a loan as the difference between the effective rateNote3 on that loan and some reference rate plus explicit fees per dollar of loan.

plj = (rlj - rj) + slj

Where plj is price for each bank l’s product, j; rlj is the interest rate for each bank l’s product, j; rj represents the reference rate, and slj represent the explicit fees.

For the purposes of the NLSPI, explicit fees are not included and we are left with simple loan margins.

plj= (rlj - rj)

Since the value of money is eroded over time, a deflation factor is applied to the spread.

This gives:

plj = (rlj - rj) * d

All variables in this equation are described above, and d is the estimated monthly value rate of the implicit price index for Final Domestic Expenditure. For a more detailed explanation of deflation see Appendix B.

5.3. Reference Rate

The reference rate that we have been discussing is defined as the theoretical opportunity cost of money. There exists no consensus, either in the literature or international practice, as to the choice of reference rate. While we have experimented with several individual reference rates as well as with a multiple reference rate approach that matches reference rates to maturities, we have opted to use a single, mixed maturity reference rate.  This method is based on the Report of the Intersecretariat Working Group on National Accounts (ISWGNA) Task Force on FISIM and the method has been reviewed by Statistics Canada’s Price Measurement Advisory Committee.

The mixed rate is constructed by taking the yields of a variety of market instruments each of which matches one of the loan maturities (See Appendix C). The yields are aggregated using the trailing 12 month funds advanced of the matching maturities as quantity weights.Note4

This brings the price calculation to:

plj = (rlj - r) * d

Where plj, rlj and d remain as described above, and where r represents the mixed reference rate.

5.4. Aggregation

The NLSPI is currently produced using a commodity-based aggregation which allows for the production of a variety of sub-indexes. Although it might be possible to provide series at a more detailed level to the CSNA, we are not currently planning to publish sub-indexes.

In order to weigh the prices from the microdata level, we use derived revenues as weights. Revenues are derived by multiplying the derived prices by the funds advanced for each product at each maturity for each bank.

Appendix A
A1 Products

Section I – Interest rates – Percentages

  • To individuals:
    • Personal loan plans
    • Personal lines of credit, secured
    • Personal lines of credit, unsecured
    • Other personal
    • Residential mortgages, insured
    • Residential mortgages, uninsured
  • Total personal loans and mortgages
  • To the business sector
    • Loans to regulated non-bank financial institutions
    • Lease receivables
    • Loans to individuals and others for business purposes
    • Non-residential mortgages
  • Total selected business loans

Section II – Funds advanced – Thousands of dollars

  • To individuals:
    • Personal loan plans
    • Personal lines of credit, secured
    • Personal lines of credit, unsecured
    • Other personal
    • Residential mortgages, insured
    • Residential mortgages, uninsured
  • Total personal loans and mortgages
  • To the business sector
    • Loans to regulated non-bank financial institutions
    • Lease receivables
    • Loans to individuals and others for business purposes
    • Non-residential mortgages
  • Total selected business loans

A2 Maturities

All
Variable rate
Fixed rate <1 year
Fixed rate 1 to <3 years
Fixed rate 3 to <5 years
Fixed rate 5 to <7 years
Fixed rate 7 years and over

Appendix B
Deflation

The APR that is reported on the A4 is the interest rate banks quote to their clients. It is equal to the effective rate if and only if there is a single interest payment made annually without compounding. The user-cost approach requires the use of effective interest rates but since those are not available in the A4, we are using the APR as a proxy. In that case, it’s important to keep in mind that the APR is a function of interest income and funds advanced:

APR = (Interest Income / Funds Advanced)

The same is also true of the assets that make up the reference rates:

Yield = (Interest income / Funds advanced)

As discussed in section 5.2, the funds advanced must be discounted to account for the decline in the purchasing power of money over time on a cumulative basis.

For the purposes of the NLSPI, we have chosen the monthly estimated Note 5 level of the implicit price index for Final Domestic Expenditure as the deflator. We then apply this value to the spread Note 6 to obtain the final price for month t:

Pt = (APRt – rt) *dt

dt = dt-1* ht

Where dt = 1 in the first month. In subsequent months, dt-1 represents the previous month’s deflator, and ht is the monthly growth rate of GDI calculated as the cubic root of the quarterly growth rate.

Annexe C
Reference rate for the NLSPI

Mixed Reference Rate

The reference rate for the NLSPI is a weighted average of the yields to maturity for the market instruments listed below. The weight of each instrument is the 12-month trailing funds advanced for the corresponding term maturity. The data is obtained from CANSIM Table #176-0043.

This table displays the results of reference rate for the nlspi. The information is grouped by maturity (appearing as row headers), market rate and cansim vector (appearing as column headers).
Maturity Market Rate CANSIM Vector
Variable Rate Overnight Rate V122514
Fixed Rate <1 year 6 month Treasury Bill V122532
Fixed Rate 1 to <3 years 1-3 year Government Bonds V122558
Fixed Rate 3 to <5 years 3-5 year Government Bonds V122485
Fixed Rate 5 to <7 years 5-10 year Government Bonds V122486
Fixed Rate 7 years and over 5-10 year Government Bonds V122486

Notes:

  1. The interest rates that are provided are in the form of Annual Percentage Rates (APRs) which is the same as the effective rate only if the loan is held for exactly one year with a single interest payment without default.
  2. See Handbook on price and volume measures in national accounts Page 30, 31.
  3. Interest Income divided by Balance of Outstanding Loans.
  4. Note that for reference year 2011; the reference rate is weighted using the fixed value of 2011 funds advanced.
  5. Since this implicit price index is only available quarterly, we take the third root of the quarterly growth rate and then multiply it by the previous month’s deflator to arrive at a cumulative monthly deflator.
  6. Deflating the funds advanced is equivalent to multiplying the spread by the monthly deflator.

2008 submissions

Linking Data for the Survey of Household Survey Spending to Income Tax Records File (T1)
2006 Farm Environmental Management Survey (FEMS): Linkage to the 2006 Census of Agriculture
Analysis of Business Behaviour
Commercial and Institutional Consumption of Energy Survey (CICES) Data Linkage: 2007 CICES
Small– and Medium–sized Enterprise Statistics (SMEStats) Data Warehouse
Omnibus Record Linkage Authority for Economic Statistics Programs
Socioeconomic Influences on the Use of Physician Services in Ontario
Self-perceived Unmet Need and Use of Health Care in Ontario
Linking Telephone Numbers to the Living in Canada Survey- Pilot Sample File
Living in Canada Survey-Pilot: Personal Income Tax Files and Pension Plans in Canada File Linkages
Census of Agriculture: Feasibility Study of Linkage to Tax Data for Replacement of Farm Financial Questions
Using 2006 Census of Population Data to Improve the Quality of the Estimates for the Access and Support to Education and Training Survey
Evaluation of the Census Internet Response Option: Linking 2006 Census Data to the Evaluation Surveys Data
Longitudinal Immigration Database and Immigrant Information in the Longitudinal Administrative Databank: 2005 to 2009 Updates
Omnibus Record Linkage Authority for Improving the Population and Household Survey Programs


Linking Data for the Survey of Household Survey Spending to Income Tax Records File (T1)

Purpose: The purpose of this linkage is to obtain income data and reduce respondent burden, interview time and collection costs for the Survey of Household Spending.  Carried out on a voluntary basis, the Survey of Household Spending gathers detailed information amounts spent on food, clothing, shelter, transportation, health care and other items in order to understand spending habits of households in Canada.  Information is also collected about dwelling characteristics and household furnishings and equipment.

The linkage allows obtaining information on income variables without burdening respondents with detailed questions about their income.  The income data is important for the Survey of Household Spending and allows analysis of the relationship between spending and income. This survey is the only Statistics Canada survey that releases information on this relationship.

Information from the Survey of Household Spending is used in the development of the Consumer Prix Index, in the System of National Accounts and is widely used in developing various federal and provincial policies and programs.

Description: The Survey of Household Spending databaseand the T1 File will be linked using the address, city, date of birth, first name, surname, sex, province, NYSIIS and SNDX code for surname, postal code, marital status, telephone number and first initial. This information will be removed from the linked file as soon as the linkage is completed, and stored separately. Access to these files will be restricted to Statistics Canada employees whose assigned work activities require access.

Output: No information containing personal identifiers will be released outside of Statistics Canada from this linkage activity. Only aggregate statistics and analysis conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada.


2006 Farm Environmental Management Survey (FEMS): Linkage to the 2006 Census of Agriculture

Purpose: To provide current information on national agri-environmental conditions and risks and to improve our understanding of on-farm environment management practices. While the FEMS data themselves can be used to meet this objective, the linked file permits a broader range of analytical studies.

Findings will aid in understanding the uptake of beneficial management practices (BMPs) and under what conditions they are being implemented. The BMPs are practices scientifically proven to reduce the impact of agricultural activities on soil and water resources while maintaining the economic viability of the industry.

The linked data are a key input into Agriculture and Agri-Food Canada’s National Agri-Environmental Health Analysis and Reporting Program (NAHARP) which, through models, provides the agriculture industry, decision-makers and the Canadian public with information on the environmental performance of Canadian agriculture. Findings may influence industry initiatives as well as federal and provincial programs and policies aimed at encouraging the sustainable agri-environmental management of air, land, manure, biodiversity and water.

The linked file will contribute to the reduction of response burden; certain important information items collected on the Census of Agriculture were deliberately left off the FEMS questionnaire. Thus, the FEMS was designed to be used along with important information collected on the Census of Agriculture

Description: To obtain information on farm operations (i.e., area farmed, number of livestock, expenditures for fertilizers, herbicides, insecticides and fungicides) and socio-economic factors, the 2006 FEMS will be linked to the 2006 Census of Agriculture. Only records for which consent was given by the 2006 FEMS respondents will be linked. The files will be linked using the Farm Register number and will not contain any direct identifiers such as name, address or telephone number.

Output: Only aggregate data that conform to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. Findings will be published in Statistics Canada’s “Agriculture and Rural Working Paper Series” (Catalogue no. 21-601-M). Aggregate statistics at the eco-region/farm type/province cross-classified levels will be produced for Agriculture and Agri-Food Canada. The linked file, without identifiers, will be retained indefinitely.


Analysis of Business Behaviour

Purpose: In order to improve the business environment and thereby enhance competitive advantage, policy makers need better information on the behaviour of businesses across the economy and over time. Administrative databases are being used to support this analysis without increasing reporting burden. These analyses will examine changes in employment, research and development expenditure, value of exports and other corporate financial data as part of a bigger project to look at business strategies and their outcomes over time. As businesses will be followed over time, changes in their characteristics will be monitored and examined in relation to changes in government programs, such as taxation, support for entrepreneurial activity, knowledge transfer, and opportunity for the highly skilled. This work will lead to the development of a longitudinal business panel survey that will be used by policy departments, such as Industry Canada, to develop government policies in a range of areas.

Description: This project links the Business Register (BR), the Longitudinal Employment Analysis Program (BR, LEAP), the Research and Development in Canadian Industry Survey, the Exporter Register and the General Index of Financial Information (GIFI) databases. The LEAP database will provide a longitudinal source of information on employment, salaries, births and deaths of enterprises. A deterministic record linkage procedure will be used to link the BR, LEAP, RDCI, Exporter Registry and GIFI datasets. The Business Number (BN) and the Business Register Identification Number (BRID) will be used as the key identifiers. The databases will be linked, starting with the 2001 data year to the 2005 data year.

Output: Only aggregate data conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. Reports will be published in the Working Paper series of the Science, Innovation and Electronic Information Division (SIEID) at Statistics Canada and an article highlighting the key findings will be published in the Innovation Analysis Bulletin of SIEID. The linked files will be retained until January 2011, at which time they will be destroyed. The linkage keys, such as BN and BRID, will be removed from the linked file and stored separately. This will allow future linkages, but removes the possibility of direct identification of a business by analysts. The linkage keys will also be destroyed by January 2011.


Commercial and Institutional Consumption of Energy Survey (CICES) Data Linkage: 2007 CICES

Purpose: The purpose of the survey is to analyze energy consumption patterns and assess how well Canada is fulfilling its commitments to increasing energy efficiency and reducing greenhouse gas emissions that contribute to climate change. These data will be used to develop and refine programs available to help businesses, institutions and organizations increase their own energy efficiency and reduce greenhouse gas emissions. To increase the quality of the survey data, energy consumption information from a knowledgeable third party is linked to the survey data when the primary respondent cannot provide this information directly.

Description: Survey respondents who cannot supply the energy data directly provide written consent to Statistics Canada to obtain their energy consumption data from a knowledgeable third party (such as their landlord or property manager), and for their data to be combined with the energy consumption data provided by the third party. The third–party contact identified by the respondent also provides written consent to combine the energy consumption data they provide with the data provided by the respondent.

This linkage covers the survey conducted for the 2007 reference year.  Linkage is only done when both the primary respondent and the third party consent.

Output: The resulting linked file, stripped of identifiers, will be retained by Statistics Canada for five years until December 2013. Information from respondents and third parties who consent both to linking and to sharing of their data will be provided, without identifiers, to Natural Resources Canada and Environment Canada, who will keep this information confidential and for an indefinite period.


Small– and Medium–sized Enterprise Statistics (SMEStats) Data Warehouse

Purpose: Small businesses have long been recognized as a key component of the Canadian economy. The SMEStats Data Warehouse will profile small– and medium–sized enterprises (SMEs) in Canada by making available key demographic statistics and performance indicators. Information of this kind is essential for policymakers to identify sectors and regions in distress and in the formulation and evaluation of tax and other policies as they impact on small– and medium–sized business in Canada. Some important policy questions this data warehouse will be used to answer include:

  • What is the continuing role of SME businesses in economic and employment growth at the provincial and sub–provincial level? In what regions and sectors are pressures on SMEs emerging?
  • What are the attributes and characteristics of the most successful of the SME businesses, the so–called “gazelles”?
  • How do SMEs compare with other businesses in terms of profit and profitability, cost structures and productivity?
  • What is the role of SMEs in job and revenue creation?
  • Do SMEs take equal or relative advantage of tax credits offered to all businesses, for example, credits intended to improve the energy efficiency of businesses?
  • Is globalization contributing to convergent or divergent growth among businesses? Is there a growing divide between large and small businesses?
  • How do Canadian businesses perform in comparison to businesses in other countries, based on internationally–accepted performance indicators?

Description: The SMEStats Data Warehouse is a cross–sectional and longitudinal linkage of Statistics Canada’s Business Register, the Longitudinal Employment Analysis Program (LEAP), Payroll Deductions file (PD7), and income and balance sheet information from T1 and T2 tax returns filed by businesses with Canada Revenue Agency, on an annual basis, for data years 2000 to 2007. This information is linked using the Business Number (BN), Statistical Enterprise Number (SNUM) and Legal/Operating Name and Postal Code.

Output: Only aggregate data conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. A brief methodology report will also be prepared explaining the database creation processes, constraints and key issues related to the quality of the data and comparisons with other data.

Outputs will include SME profiles consisting of counts of enterprises by employment and revenue size grouping by industry (NAICS) and geography down to fine sub–provincial areas; and age of business. SME performance indicators will focus on issues related to firms, employment and wealth. Additional ad hoc analyses may also take place. The linked files will be retained until January 2011, at which time they will be destroyed.


Omnibus Record Linkage Authority for Economic Statistics Programs

Purpose: To facilitate the efficient and effective production of high-quality statistical outputs from Statistics Canada’s economic statistics programs.

Statistics Canada’s strategy for its economic statistics programs is heavily reliant on the mix of survey data and administrative data. This approach yields higher quality statistics, for less cost and with less burden on Canadian businesses, as information reported for administrative purposes to public or private organizations is also used by Statistics Canada for statistical purposes.

Record linkages can be for three purposes:

  • Data production:  Use already collected data for a statistical program rather than re-collect.  It also includes frame creation, preparation of contact material, imputation for item non-response.
  • Analysis to support production:  Analysis for purposes of data certification and data quality evaluation, such as evaluating trends in one data set by examining the reports of the same businesses in another data set.
  • Analysis to provide information:  Making use of combined datasets to support analysis that is not possible by a single existing data set. In most cases, the outputs from these projects are officially released.

Statistics Canada has adopted this strategy following consultation with a wide range of businesses.  Generally, record linkages are approved on a project-by-project basis.  However, given the fundamental importance of linkage in the economic statistics programs, linkages are approved under this omnibus authority, subject to the following considerations:

  • No linkage should damage Statistics Canada’s relationship with respondent businesses.
  • No privacy-invasive linkages shall be carried out without a demonstrated public good. Privacy concerns are present when information from unincorporated businesses is linked.
  • If the linkage involves client-specified group of businesses, no files are to be linked if the results might harm the interests of that group.

The following are excluded from the umbrella of the omnibus record linkage authority:

  • Linkages that would have significant privacy implications, including all activities of the Agriculture Division, and targeted linkages related to small and/or unincorporated businesses
  • Linkages that involve client-supplied lists of businesses
  • Linkages that present a significant risk of residual disclosure

Description: For a particular program, linkages can be one or more of the following:

  • Survey data to administrative data for the same time period.
  • Survey data for a specific time period to the same survey data from a different time period.
  • Survey data for a specific time period to data from another survey for the same time period.
  • Administrative data to another administrative file.

In this manner, both cross-sectional and longitudinal linkages are possible.

Survey data refer to data collected directly from businesses by Statistics Canada.  Administrative data refer to data collected by other organizations as part of their mandate.  Statistics Canada uses administrative data for statistical purposes.  Although not the only such files, the primary administrative files used for these purposes are tax files collected by the Canada Revenue Agency.

Usually, linkage is conducted using Business Numbers and other similar identifiers.

Output: All outputs will conform to the legal/confidentiality requirements of the Statistics Act, and any other federal legislation that may apply. Retention periods for linked files vary from project to project. Files that are produced for survey-production purposes generally have indefinite retention.  On the other hand, files linked for a specific project will be retained only until the end of the project. Direct identifiers (such as names, addresses) are usually not be maintained on linked files, but will be maintained separately. As of July 1, 2008, Statistics Canada will maintain an inventory of all linkages conducted under this omnibus authority.


Socioeconomic Influences on the Use of Physician Services in Ontario

Purpose: To examine whether the socioeconomic status of patients influences their use of general practitioner (GP) services, their referral patterns to specialists, and their joint use of different physicians’ services. Actual, rather than self-reported, measures of physician utilization will be employed. More specifically, the research will:

  1. assess whether use of GP services, measured by the number of visits, the type of services and the related expenditures, varies with  patients’ socioeconomic status (measured by income and education), after taking health care needs (measured by self-reported health status) into account;
  2. model the pathway between patients’ use of GP services and their use of specialist services, taking into account socioeconomic status differences and health care needs; and,
  3. determine whether certain categories of survey respondents systematically over- or under-report the number of physician visits they made in the year prior to the survey.

This study is part of a PhD candidate’s dissertation and findings will provide valuable information to the medical community and to health policy makers. The results may indicate that certain population groups are disadvantaged by the current delivery of health care, in which case the study will indicate where changes should be made

Description: Data from respondents to the Canadian Community Health Survey (CCHS), cycle 1.1 (2000-2001) will be linked to administrative information on visits to physicians and health services received in Ontario. Only those cases where informed consent was received from survey respondents will be linked.

The administrative databases used in this research project are the Medical Services files – based on Ontario Health Insurance Program (OHIP) claims for three fiscal years: 1999-2000, 2000-2001, and 2001-2002.

The data will be linked using deterministic matching on an encrypted health number. A validation procedure, carried out by the Ontario Ministry of Health and Long-Term Care (MOHLTC), has made sure only valid health card numbers for CCHS records are found on the cohort file, and are encrypted. Health card numbers have been similarly encrypted on the health administrative databases. Personal identifiers will be removed from the files and a number assigned by Statistics Canada will be appended to records in the administrative databases that link to CCHS records.

The study is part of a pilot project between Statistics Canada, the MOHLTC and McMaster University, aimed at enhancing access to Ontario health information by the research community. The creation of an analytical file, as well as aggregation and analysis of the data, will be carried out in the Statistics Canada Research Data Centre (RDC) at McMaster University. The researcher accessing the data in the RDC will do so as a deemed employee of Statistics Canada.

Output: Only aggregate statistical outputs conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. Research findings will be disseminated through a release in The Daily, research papers, peer-reviewed journal articles or through presentations at national or international conferences. Statistics Canada will retain the linked files for a period of five years, that is, until September 2013, at which time they will be destroyed.


Self-perceived Unmet Need and Use of Health Care in Ontario

Purpose: The study will show what types of Ontario residents experience unmet health care needs, and examine the relationship between the reasons for the unmet need and the use of health care services. The overall objective of this study is to characterize how the use of physician and hospital care by those who report an unmet health care need compares to similar individuals who do not report an unmet health care need.

The relationship between reporting an unmet health care need and overall physician and hospital use will be examined. As well, the relationship between the reason for the unmet need, that is, due to personal choice, to system barriers, to wait times, or for other reasons, and the amount and type of physician or hospital services received will also be examined. Findings may be used by the health care system to better focus its resources to address needs that come from system limitations.

Description: Data from respondents to the Canadian Community Health Survey (CCHS), cycle 1.1 (2000/2001) will be linked to administrative information on diagnoses, health care services received, and expenditures for those respondents who visited physicians, stayed in hospitals or underwent outpatient procedures in Ontario hospitals. Only those cases where informed consent was received from survey respondents will be linked.

Two administrative databases are used in this research project: 1) the Medical Services files – based on Ontario Health Insurance Program (OHIP) claims for 1999/2000, 2000/2001 and 2001/2002; and the Discharge Abstract Database (DAD) inpatients and day procedures for 1999/2000, 2000/2001 and 2001/2002.

The data will be linked using deterministic matching on an encrypted health number. A validation procedure, carried out by the Ontario Ministry of Health and Long-Term Care (MOHLTC), has made sure only valid health card numbers for CCHS records are found on the cohort file, and are encrypted. Health card numbers have been similarly encrypted on the health administrative data files. Personal identifiers will be removed from the files and a number assigned by Statistics Canada will be appended to records in the administrative databases that link to CCHS records.

The study is part of a pilot project between Statistics Canada, the Ontario MOHLTC and McMaster University, aimed at enhancing access to Ontario health information by the research community. The creation of an analytical file, as well as aggregation and analysis of the data, will be carried out in the Statistics Canada Research Data Centre (RDC) at McMaster University. Researchers accessing the data in the RDC will do so as deemed employees of Statistics Canada.

Output: Only aggregate statistical outputs conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. Research findings will be disseminated through a release in The Daily, working papers, articles in peer-reviewed journals and presentations at national and international conferences. Statistics Canada will retain the linked files for a period of five years, that is, until September 2013, at which time they will be destroyed.


Linking Telephone Numbers to the Living in Canada Survey- Pilot Sample File

Purpose: To improve response rates and population coverage for the pilot survey and to reduce costs.

The Living in Canada Survey-Pilot (LCS-Pilot) is a longitudinal survey that will test the survey content of the Living in Canada Survey (LCS). The LCS is Canada’s first comprehensive long-run socio-economic panel survey of households. The information collected will serve as the basis for a better understanding of Canadian society in the 21st century.  It emphasizes the relationships between four major domains of people’s lives—work, family, education and health—and allows for research in areas important to Canadians. Moreover, the results of this survey will be used by different levels of government in order to help them develop better policies and programs. Surveys such as this one have already proven to be valuable in other countries, enabling Canada to compare itself internationally on many important issues.

This data linkage activity will associate telephone numbers with the addresses of dwellings selected for the LCS-Pilot sample. These linked telephone numbers will be used during collection activities to contact households. This linkage activity will facilitate contact, improve response rates and reduce interviewing costs for the LCS-Pilot.

Description: The sample file for the LCS-Pilot will be linked to local telephone company files (also called billing files) and to the InfoDirect database. Addresses (including street number, street name, unit number, city, province and postal code) will be matched on these files, and the telephone number will be extracted to facilitate contact with dwellings. Only publicly available telephone numbers will be used in this linkage activity. This linkage will be performed once in the fall of 2008.

Output: The output from this linkage activity will only be used within Statistics Canada for collection purposes for the LCS-Pilot. Results of this linkage will be retained for the life of the LCS-Pilot field period (up to 2014 in the current plans) to facilitate contact with households in subsequent waves of this panel survey.


Living in Canada Survey-Pilot: Personal Income Tax Files and Pension Plans in Canada File Linkages

Purpose: To improve the quality of the data collected for the survey and to reduce response burden and survey costs.

The Living in Canada Survey-Pilot (LCS-Pilot) is a voluntary longitudinal survey that will test the survey content and methodology of the Living in Canada Survey (LCS). The LCS is Canada’s first comprehensive long-run socio-economic panel survey of households. It emphasizes the relationships between four major domains of people’s lives—work, family, education and health—and allows for research in areas important to Canadians. Moreover, the results of this survey will be used by different levels of government in order to help them develop better policies and programs. Surveys such as this one have already proven to be valuable in other countries, enabling Canada to compare itself internationally on many important issues.

The linkages will allow for the collection of some variables that are difficult for respondents to remember accurately, such as pension plan contributions, or income taxes paid. The linkages will add retrospective income data which complements retrospective education, family and work data already collected in the survey.

The linkages will also replace more than 20 income questions on the survey. Respondents are asked to report detailed information on income from earnings, investments, government sources, and pensions. To shorten the interview and reduce response burden, respondents are given the option to grant Statistics Canada permission to access their tax records for the duration they remain within the survey. The linkage will also reduce response burden to the survey by allowing use of high quality administrative data on income, earnings and employers for past years, which eliminates the need to ask these in separate questions.

In addition, the resulting survey data will be of higher quality as administrative income data has been judged to be of better quality than self-reported income.

Description: Data linkages will only be made for those respondents who give Statistics Canada their consent to access their tax files. For these respondents, their income, earnings and employer information will be retrieved from the T1 tax file, and the T4 Summary and Supplementary files. To find the individual’s Social Insurance Number (SIN), the respondents first and last name, sex, date of birth, marital status, address and postal code, the presence of the respondent’s spouse in the household and the date of birth of the respondent’s spouse (if the respondent’s spouse also granted Statistics Canada permission to link to their tax records) will be used to match to the T1 file.

Using the SIN, a linkage will be made to T1 and T4 data for the each collection year (2007 through 2012) of the longitudinal pilot survey, and all previous calendar years going back to 1990. The employer pension plan identifier from the respondent’s T4 will be used to link information on the respondent’s employer pension plan from the Pension Plans in Canada File for each collection year only.

A separate linking key file containing the respondent’s SIN and other relevant personal identifiers will be retained for the life of LCS-Pilot processing (to the end of 2015).

Output: The data resulting from the linkage will be retained indefinitely by Statistics Canada’s Income Statistics Division. These data will have all personal identifiers removed and will be held with the rest of the content from the LCS-Pilot. All information released outside of Statistics Canada will conform to the confidentiality provisions of the Statistics Act. The linked income, tax, employment and pension data will be maintained in the LCS-Pilot database along with the respondent’s interview-obtained information. Other outputs from the project will include a summary of the pilot results, and other methodology-related reports.


Census of Agriculture: Feasibility Study of Linkage to Tax Data for Replacement of Farm Financial Questions

Purpose: This study will determine the feasibility of replacing respondent-provided farm financial data with tax data in the 2016 and future Censuses of Agriculture, with the goals of reducing response burden and improving the quality of farm financial data. For the study, Statistics Canada will link farm business tax data from the Statement of Farming Activities of T1 and T3 tax filers, and the income statement and balance sheet information for T2 filers, as well as the T4 Summary report, to the 2006 Census of Agriculture, the 2009 Census Test, and the 2011 Census of Agriculture.  The Farm Register will be used to facilitate tax and Census of Agriculture data linkage.

The results of this feasibility study will be used to develop procedures and systems for the proposed 2016 Census of Agriculture-tax data linkage. Aspects to be tested in the linkage include linkage methods, linkage keys, match rates, data quality, and willingness of farm operators to provide their Canada Revenue Agency (CRA) business number on the Census of Agriculture questionnaire for data linkage and data replacement purposes.

Description: Tax data on farm business revenue, expenses and payroll corresponding to farm financial questions on the Census of Agriculture will be obtained from Canada Revenue Agency files for farm businesses. The tax data will be evaluated for comparability to Census of Agriculture farm financial data collected from farm operators in the 2006 Census of Agriculture, the 2009 Census Test, and the 2011 Census of Agriculture and content test questionnaires.

Respondents to the 2009 Census Test and the 2011 Census of Agriculture will be requested to provide their farm CRA Business Number and will be notified of the linkage to tax data by means of a statement on the back of the Census form.

Output: Results of the research project will be used for evaluation, operational planning and quality assurance purposes. Outputs will include research or technical papers discussing the results of the research project. Linkage-based data from this study will not be disseminated as part of the initial 2011 Census of Agriculture database release.

If the study concludes that tax data replacement is not feasible for 2016, the linked files will be retained only until May 2017, at which time they will be destroyed. Should the study be successful, a decision will be taken at a later stage about whether tax data replacement will be implemented for 2016. At that time, a decision will also be taken as to whether the files that were linked for the feasibility study will be retained indefinitely, in order to furnish a data set that will be conceptually comparable to 2016 data.


Using 2006 Census of Population Data to Improve the Quality of the Estimates for the Access and Support to Education and Training Survey

Purpose: To improve the data quality of the Access and Support to Education and Training Survey (ASETS). ASETS is a new voluntary survey whose objective is to assess education and training demand in Canada in the context of life-long learning. The survey will provide information on access to post-secondary education (PSE), the role of student loans and savings in the financing of PSE, and participation in adult education and training. It will also provide information on special populations like aboriginal persons living off-reserve, recent immigrants and official language minorities. The data collected by ASETS will help to monitor preparedness and access to education, evaluate the effectiveness of government education-related programs and develop policies to deal with the training needs of Canadians. As such, it is important that the statistics generated from the survey be as accurate as possible.

The survey is based on a sample of 72,000 households. Despite extensive efforts made by the survey to maximize the response rate, residents in some dwellings could not be contacted and others chose not to participate in the survey. Thus, some nonresponse remains and the potential for bias in the survey estimates is significant, even if the differences between respondents and nonrespondents are small. The objective of the linkage is to use 2006 Census microdata as auxiliary information to improve the ASETS nonresponse adjustment methodology, reduce potential bias and ultimately improve the quality of the survey estimates.

Description: The survey selects its sample from a telephone list that was built using two sources: microdata from the 2006 Census of Population complemented by the list of telephone numbers used for Random Digit Dialling surveys. Sampled dwellings will be linked to the corresponding Census data to determine suitable classes to perform nonresponse adjustments for ASETS.

Output: Only aggregate information conforming to the confidentiality provisions of the Statistics Act will be released outside of Statistics Canada. Nonresponse classes and related variables will be kept internally, on the weighting file, which will be retained until December 31, 2011 at which time this file will be destroyed. The 2006 Census variables and nonresponse class identifiers will not be part of the ASETS master file, which will be kept indeterminately by Statistics Canada.


Evaluation of the Census Internet Response Option: Linking 2006 Census Data to the Evaluation Surveys Data

Objective: Allow Statistics Canada to offer Canadians an Internet response mode for the Census that will be secure and practical for respondents, and generate the highest quality data possible. This study will assist in establishing a promotion strategy for Internet response. In other words, establish a method to identify geographic areas where households will receive a letter requesting that they complete their questionnaire on-line without having received the questionnaire in advance.

The 2006 Census offered an Internet response option for the first time to all Canadian private households. It is expected that the participation rate (approximately 18% in 2006) will increase as households get connected and as respondents feel more at ease with this medium. With the 2011 Census it is expected that the Internet will probably be one of the main methods for collecting data.

In Canada, the quinquennial census is the sole source of reliable detailed data for small areas such as neighbourhoods and for small groups such as single parent families, immigrants, as well as industrial and professional categories. All levels of government, as well as enterprises, industries, the media, academia and independent organizations benefit from census data which are essential to their decisions regarding many public services.

Federal transfer payments that are calculated in establishing various provincial and territorial programs are based on census population counts. Several provincial and regional governments also use population counts to grant subsidies to local and municipal governments.

Governments use census data extensively in developing their plans and policies in sectors such as employment, schools, training, transportation, housing, immigration and income support. Urban planners and community infrastructure planners rely on census data to plan for schools, roads, water supply systems, public transport, fire protection, and future requirements for housing, hospitals and daycare.

Description: The 2006 Census data will be matched with the data collected through three evaluation surveys that will assess the 2006 Internet response. These surveys were conducted on a voluntary basis among 5200 respondents to the 2006 Census. The respondents were informed that to reduce the number of questions asked, information obtained during the Census would be added to the evaluation surveys data for analysis purposes. The data will be matched using a household identification number.

Product: The aggregate data will be presented in a Statistics Canada internal report. The linked files will be kept until May 31, 2011, and then destroyed.


Longitudinal Immigration Database and Immigrant Information in the Longitudinal Administrative Databank: 2005 to 2009 Updates

Purpose: To provide information on the economic integration of immigrants into Canadian society. The Longitudinal Immigration Database, known as IMDB, enables the federal and provincial governments involved in immigration issues and programs, as well as the research community and immigrant settlement agencies in Canada, to conduct research regarding the selection of immigrants, their settlement patterns and their economic integration into Canadian society. The IMDB is the only source of data that can support research on the impact of immigration policy levers such as the category of admission, selection criteria and special admissions program, on economic outcomes, and is the only source of longitudinal data with a sufficient sample of immigrants to examine settlement trajectories and integration patterns over time and by characteristics at arrival and selection criteria.

Since the Census of Population does not collect information on the human capital of immigrants upon their arrival to Canada or policy levers in effect at that time, the IMDB is unique in addressing specifically the impact of admission categories and foreign credentials on economic integration.

The addition of data for the 2005 to 2009 period in the IMDB will continue to enhance the database in order to inform immigration policy and programs.  In particular, the information will allow the analysis of the economic outcomes of immigrants who came to Canada during the 1990s and 2000s, as well as the evaluation of immigrant selection under the 2002 Immigration and Refugee Protection Act.

The IMDB is supported by a federal-provincial consortium, led by Citizenship and Immigration Canada, that includes Human Resources and Social Development Canada and the provincial governments of Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia. These federal departments and provincial governments have worked together for a number of years to direct the development of the IMDB and to support the development and maintenance of the database at Statistics Canada.

The linkage and analysis will be conducted in Statistics Canada’s offices. The linked files will not be available outside Statistics Canada.

Description: The IMDB links immigration and immigrant landing administrative files with information from tax files of immigrants who are granted the right to live permanently in Canada. Currently, the database includes records of immigrants who arrived in Canada from 1980 to 2004. Information for those who arrived in Canada from 2005 to 2009 and new tax data on those immigrants already in the database will be added as the data become available. Once an individual immigrant’s data have been successfully matched for the first time, the record for this person is retained in the IMDB and continues to be matched annually to tax files for up to 16 years. Once the linkage is done, all names and Social Insurance Numbers are removed from the analysis files and stored separately on password-protected files. Also, immigration identifier and landing information are provided annually to the Longitudinal Administrative Databank (LAD) program of Statistics Canada.

Output:  Only aggregate data conforming to the confidentiality provisions of the Statistics Act are released outside of Statistics Canada. The IMDB will be retained until the next review, planned for 2011, to ensure its continued relevancy to immigration policy development. The immigration identifier and landing information are retained indefinitely on the LAD.  Both the IMDB and the LAD are registered with the Treasury Board of Canada as Personal Information Banks, namely PPU 135 and PPU 112, respectively.


Omnibus Record Linkage Authority for Improving the Population and Household Survey Programs

Purpose: To assess and improve the quality of statistical outputs from Statistics Canada’s population and household survey programs; to reduce survey costs and reduce response burden for household surveys.

Statistics Canada carries out record linkage activities as part of its population and household survey programs. Linkages covered by this omnibus authority have one of three purposes:

  1. To obtain information that benefits a survey, such as for stratification in survey design, but that does not directly contribute to estimates;
  2. To study and assess survey data quality, for example, by comparing survey data to data from other sources; and
  3. To aid in data collection, such as to provide addresses to mail introductory letters or to provide telephone numbers to reduce collection costs by permitting data collection through a telephone interview.

These types of linkages do not contribute directly to statistical outputs that are disseminated outside Statistics Canada. Linkages that do contribute directly to statistical outputs for public release are not covered here and require separate approval.

Description: The linkages can be survey file to survey file, survey file to administrative file, or administrative file to administrative file. The linkages covered by this authority do not introduce a disclosure risk since they do not involve activities to directly produce statistical outputs.

Output: Linkages will not result in statistical outputs for dissemination outside Statistics Canada. For a given survey, no data from sources outside the survey, obtained from a linkage, will be used as individual values for estimation.

Linked files will be held solely within Statistics Canada, and will be retained only for the duration of the project (at most a few years). As of November 1, 2008, Statistics Canada will maintain an inventory of all linkages conducted under this omnibus authority.

Public consultation and notices - Occupational classifications

Permanent consultation process for the NOC 

National Occupational Classification (NOC)

NOC 2021

NOC 2016

NOC 2011

Revision of the North American Industry Classification System (NAICS) Canada 2012

July 30, 2013 (Previous notice)

The North American Industry Classification System (NAICS) is currently being revised. The revised NAICS will be available in 2017, and will be known as NAICS 2017.

At this time, Statistics Canada is soliciting input from data producers and data users to ensure their needs continue to be met by NAICS. Proposals for changes to NAICS should be submitted to standards-normes@statcan.gc.ca. Guidelines for the revision of NAICS classes are presented below to assist you in providing input into the NAICS revision process.

Input is requested by July 31, 2014. Decisions on proposed revisions will be made by January 2016, following a review within Statistics Canada, with other government departments and non-government entities, and with our counterparts at the Mexican Instituto Nacional de Estadistica y Geografia (INEGI) and the Economic Classification Policy Committee (ECPC) of the United States, acting on behalf of the Office of Management and Budget.

Statistics Canada, INEGI and ECPC collaborate on NAICS to make the industry statistics produced by the three countries comparable; they will continue to do so for NAICS 2017.

Guidelines

Submissions may be made for any industry, existing or newly created. Proposals for the modification of an existing industry must contain information on the rationale for the change and demonstrate an improvement to its definition. Proposals for the addition of a new industry must contain information on the grouping criteria for creating the industry, the production function, the relative size of the proposed industry and its economic significance. This input will also be used to assess confidentiality issues and costs of change to data producers and data users, and to negotiate with Mexico and the United States.

You may send more than one submission, if that enables you to comment earlier.

NAICS Canada 2012 may be viewed at: North American Industry Classification System (NAICS) 2012.

Please consider the following criteria when preparing your input to the revision of the North American Industry Classification System.

Criteria for creating new classes or updating current classes

The criteria for creating new classes or updating current classes are as follows. Proposed classes should:

  1. Meet the process-based conceptual framework agreed to by Canada, Mexico and the United States for grouping producing units. The principle underlying NAICS is that units that have similar production functions should be grouped together in the classification.
  2. Be consistent with classification principles of mutual exclusivity, exhaustiveness, and homogeneity of units within classes.
  3. Have empirical significance, that is, classes should produce gross revenues of $500 million, be collectable and publishable, and linked to a funded program for data collection.
  4. Be relevant, that is, they must be of analytical interest, result in data useful to users and be based on appropriate statistical research and subject matter expertise.
  5. Be given special attention as far as the following industries are concerned and for which lower revenue requirements will be considered:
    • new and emerging industries;
    • services industries;
    • industries engaged in the production of advanced technologies.

Use of the North American Product Classification System (NAPCS)

Users of economic classifications may want to consider and evaluate whether their needs are better met with a product classification rather than an industry classification. NAICS classifies units according to their production function, resulting in groupings of units that do similar activities using similar resources but not necessarily in groupings of similar products or outputs. The North American Product Classification System (NAPCS) is a classification that organizes the goods and services produced by the establishments, within a demand-based conceptual framework. Statistical needs may be better met with product data crossing industries rather than with the creation of a new industry. Proposals for changes to NAICS will be evaluated within the context of both the industry and product classification systems.

New Dissemination Model – Navigation and Tables

Consultation objectives

In April 2012, Statistics Canada launched its three-year New Dissemination Model project with the goal of modernizing the methods and framework for disseminating data via its website. The key objective is to create a user-centric website and to increase coherency, consistency and simplicity in dissemination activities.

Statistics Canada will be consulting Canadians in October 2013 about the new website design. The objective of the consultation is to determine whether Statistics Canada website users find the New Dissemination Model's proposed navigation framework, taxonomy and tables structure intuitive and easy to use. Some tasks will be tested on mobile devices or emulators.

Consultation methodology

Statistics Canada will hold in-person usability consultations. Participants will be asked to complete a series of tasks and to provide feedback on the proposed website.

How to get involved

Individuals who wish to obtain more information or to take part in a consultation may contact Statistics Canada by sending an email to consultations@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.

Results

Results will be posted online when available.

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